Utilizing artificial neural networks to evaluate routes based on generated route tiles

ABSTRACT

This disclosure covers methods, non-transitory computer readable media, and systems that generate route tiles reflecting both GPS locations and map-matched locations for regions along a route traveled by a client device associated with a transportation vehicle. For example, in some implementations, the disclosed systems use an artificial neural network to analyze the route tiles and determine route-accuracy metrics indicating GPS locations or map-matched locations for particular regions along the route. The disclosed systems can then use the route-accuracy metrics to facilitate transport of requestors by, for example, determining a distance of the route or a location of a client device associated with a transportation vehicle.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.15/858,775, filed on Dec. 29, 2017. The aforementioned application ishereby incorporated by reference in its entirety.

BACKGROUND

Transportation routing systems increasingly use tracking technologies todetermine the location and routes traveled by vehicles and computingdevices carried by such vehicles. For instance, conventionaltransportation routing systems use computing devices that employ GlobalPositioning System (“GPS”) to determine traffic patterns, driver habits,or digital maps of a road network. Tracking devices that use GPS canoften improve or provide the data for determining vehicles' locations,routes, and patterns.

Despite improving route detection, conventional GPS-based routingsystems sometimes generate or receive inaccurate GPS data. For instance,conventional GPS-based routing systems often generate or receive noisyGPS data indicating incorrect locations of a computing device along aroute. Such GPS data may indicate locations within a route that wouldrequire a vehicle to accelerate or travel at magnitudes that the vehiclecould not exhibit based on standard driving patterns or the laws ofphysics. For example, noisy GPS data sometimes indicates a vehiclerapidly changing from one latitudinal or longitudinal location toanother such location, such as at an intersection with a traffic light.Several factors may cause noisy GPS data, such as a faulty GPS device,the landscape of the area in which a computing device is located, or thespeed at which the computing device travels. Accordingly, conventionalGPS-based routing systems sometimes inaccurately determine a vehicle'slocation or route based on noisy GPS data.

To correct for the inaccuracies of GPS data, some conventional map-matchrouting systems adjust GPS data to predict a vehicle's location withinroads of a digital map. For instance, some conventional map-matchrouting systems use GPS data from a computing device to estimatelocations along roads of a digital map. But conventional map-matchrouting systems require up-to-date maps, among other things, toaccurately determine a vehicle's location and routes traveled.Developing such digital maps often requires collecting satelliteimagery, managing a mapping fleet of vehicles, and purchasing largeamounts of traffic data.

Notwithstanding the expense and difficulty of creating digital maps,conventional map-match routing systems often inaccurately determine thelocation and routes of vehicles. For example, conventional map-matchrouting systems sometimes determine a vehicle is located on anon-existent (or incorrect) road based on (i) algorithms thatinaccurately match GPS data to roads or (ii) faulty digital maps. Afterconstruction of a new road, for example, conventional map-match routingsystems may take months (or more) to identify the new road and updatethe map using a minimum number of observed-data points and an offlineprocess for adjusting the map.

Both conventional GPS-based routing systems and conventional map-matchrouting systems pose significant technical limitations for on-demandservice matching systems that provide transportation vehicles torequestors. On-demand service matching systems often need to accuratelydetermine a location and route of a vehicle in real time (or near realtime) to match transportation vehicles with requestors and to accuratelydetermine prices for transport. The inaccuracy and slow adjustmentmechanisms of conventional GPS-based routing systems and conventionalmap-match routing systems inhibit such on-demand service matchingsystems from determining locations, distances, and/or routes fortransport.

SUMMARY

This disclosure describes one or more embodiments of methods,non-transitory computer readable media, and systems that solve theforegoing problems in addition to providing other benefits. For example,in one or more embodiments, the disclosed systems generate route tilesreflecting both GPS locations and map-matched locations for regionsalong a route traveled by a client device associated with atransportation vehicle. In some implementations, the disclosed systemsuse an artificial neural network to analyze the route tiles anddetermine route-accuracy metrics indicating GPS locations or map-matchedlocations for particular regions along the route. The disclosed systemscan then use the route-accuracy metrics to facilitate transport ofrequestors by, for example, determining a distance of the route or alocation of a client device associated with a transportation vehicle.

In some embodiments, for instance, the systems identify both trainingGPS locations and training map-matched locations for a training routetraveled by a client device associated with a transportation vehicle.The systems can further generate regions along the training route. Forinstance, a region can include a subset of training GPS locations and asubset of training map-matched locations. Based on the subset oftraining GPS locations and the subset of training map-matched locations,the systems generate a training route tile for the region. The systemsthen use an artificial neural network to predict a route-accuracy metricfor the region based on the training route tile. By comparing theroute-accuracy metric to a ground-truth-route-accuracy metric for theregion, the systems train the artificial neural network. In addition totraining the artificial neural network, in certain embodiments, thesystems use the network to analyze route tiles corresponding to a routetraveled by a client device and to determine route-accuracy metrics forregions along the route.

The disclosed systems avoid the deficiencies of conventionaltransportation routing systems. By using an artificial neural network topredict route-accuracy metrics for route tiles, the disclosed systemscan more accurately determine distances traveled by (or locations of) aclient device associated with a transportation vehicle. Unlikeconventional transportation routing systems, the disclosed systems avoidthe inaccuracies of noisy GPS data and map-matched data by determiningroute-accuracy metrics for specific regions along a route, whichindicate to the systems to rely on GPS locations or map-matchedlocations for particular regions. In addition, unlike the rigidity ofconventional map-match systems, the disclosed system's artificial neuralnetwork creates a more flexible analysis of route distances, estimatedpaths, and/or client device locations.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description refers to the drawings briefly described below.

FIG. 1 illustrates a block diagram of an environment for implementing anintelligent transportation routing system in accordance with one or moreembodiments.

FIGS. 2A-2C illustrate GPS locations corresponding to a route,map-matched locations corresponding to the route, and a ground-truthrepresentation of the route in accordance with one or more embodiments.

FIGS. 3A-3B illustrate regions along a route corresponding to GPSlocations and map-matched locations, a particular region along the routecorresponding to GPS locations and map-matched locations, and a routetile in accordance with one or more embodiments.

FIG. 4 illustrates a sequence-flow diagram of an intelligenttransportation routing system training an artificial neural network touse training route tiles to predict route-accuracy metrics for regionsalong a training route traveled by a client device in accordance withone or more embodiments.

FIG. 5 illustrates layers of a convolutional neural network foranalyzing route tiles in accordance with one or more embodiments.

FIG. 6 illustrates a sequence-flow diagram of an intelligenttransportation routing system using an artificial neural network todetermine route-accuracy metrics for regions along a route traveled by aclient device in accordance with one or more embodiments.

FIG. 7 illustrates a sequence-flow diagram of an intelligenttransportation routing system generating training route tiles andground-truth-route-accuracy metrics based on simulated route locationsand simulated training GPS locations in accordance with one or moreembodiments.

FIG. 8 illustrates a graphical user interface of a client devicepresenting a distance determination for a route traveled by a clientdevice in accordance with one or more embodiments.

FIGS. 9A-9B illustrate an adjustment of a digital map for a route inaccordance with one or more embodiments.

FIG. 10 illustrates a schematic diagram of the intelligenttransportation routing system in accordance with one or moreembodiments.

FIG. 11 illustrates a flowchart of a series of acts for training anartificial neural network to use training route tiles to predictroute-accuracy metrics for regions along a training route traveled by aclient device in accordance with one or more embodiments.

FIG. 12 illustrates a flowchart of a series of acts for using anartificial neural network to determine route-accuracy metrics forregions along a route traveled by a client device in accordance with oneor more embodiments.

FIG. 13 illustrates a block diagram of a computing device in accordancewith one or more embodiments.

FIG. 14 illustrates an example environment for an intelligenttransportation routing system in accordance with one or moreembodiments.

DETAILED DESCRIPTION

This disclosure describes one or more embodiments of a transportationrouting system that utilizes an artificial neural network to evaluateroutes based on generated route tiles. For example, in one or moreembodiments, the intelligent transportation routing system analyzes aroute traveled by a client device associated with a transportationvehicle and generates route tiles representing GPS locations andmap-matched locations for regions along the route. In someimplementations, the intelligent transportation routing system inputsthe route tiles into an artificial neural network to determineroute-accuracy metrics indicating GPS locations or map-matched locationsfor particular regions along the route. A route-accuracy metric may, forexample, indicate a noise level for a subset of GPS locationscorresponding to a region of the route. In certain embodiments, theintelligent transportation routing system uses the route-accuracymetrics to facilitate transport of requestors by determining a distanceof the route or a location of a client device associated with atransportation vehicle.

In some embodiments, for instance, the intelligent transportationrouting system identifies both training GPS locations and trainingmap-matched locations for a training route traveled by a client deviceassociated with a transportation vehicle. The intelligent transportationrouting system can further generate regions along the training route.For example, the intelligent transportation routing system can generatea region comprising a subset of training GPS locations and a subset oftraining map-matched locations. Based on the subset of training GPSlocations and the subset of training map-matched locations, theintelligent transportation routing system can generate a training routetile for the region. In one or more embodiments, the intelligenttransportation routing system then uses an artificial neural network topredict a route-accuracy metric for the region based on the trainingroute tile. By comparing the route-accuracy metric to aground-truth-route-accuracy metric for the region, the intelligenttransportation routing system can train the artificial neural network.

In addition to training an artificial neural network, the intelligenttransportation routing system can also use an artificial neural networkto analyze route tiles to determine route-accuracy metrics. For example,in some embodiments, the intelligent transportation routing systemidentifies a set of GPS locations and a corresponding set of map-matchedlocations for a route traveled by a client device. The intelligenttransportation routing system then segments the set of GPS locations andthe corresponding set of map-matched locations into regions along theroute. Each such region comprises a subset of GPS locations and a subsetof map-matched locations. In some embodiments, the intelligenttransportation routing system generates route tiles for individualregions along the route.

In one or more embodiments, the intelligent transportation routingsystem then uses the trained artificial neural network to determine aroute-accuracy metric for each region. The intelligent transportationrouting system further selects, for each region, one of the subset ofGPS locations or the subset of map-matched locations based on theroute-accuracy metric. The intelligent transportation routing systemdetermines updates the route to include the selected subset of GPSlocations or the selected subset of map-matched locations for eachregion. For example, the intelligent transportation routing systemupdates the route by determining a distance of the route based on theselected subset of GPS locations or the selected subset of map-matchedlocations for each region. Additionally, or alternatively, theintelligent transportation routing system updates the route by adjustinga digital map for the region to correspond to the selected subset of GPSlocations or the selected subset of map-matched locations for eachregion. Moreover, in some embodiments, the intelligent transportationrouting system updates the route by determining a location of the clientdevice based on a route-accuracy metric.

As just mentioned, the intelligent transportation routing system canutilize training GPS locations and training map-matched locations togenerate training route tiles and a trained neural network. Toillustrate, in certain embodiments, a training route tile includes twoimage matrices. In particular, a first image matrix can includerepresentations of a subset of training GPS locations for a region. Inaddition, a second image matrix can include representations of atraining subset of map-matched locations for the region.

As noted above, in certain embodiments, the intelligent transportationrouting system also inputs training route tiles into an artificialneural network to predict a route-accuracy metric for a region. Theroute-accuracy metric may take different forms to indicate a subset oftraining GPS locations (or a subset of training map-matched locations)for a particular region. For example, in some implementations, aroute-accuracy metric may be an accuracy classifier that indicates apredicted noise level for a subset of training GPS locationscorresponding to a region along the training route.

When training the artificial neural network, in some embodiments, theintelligent transportation routing system compares aground-truth-route-accuracy metric to a predicted route-accuracy metricfor a given region's training route tile. Based on the comparisonbetween the ground-truth-route-accuracy metric and the predictedroute-accuracy metric, the intelligent transportation routing systemadjusts parameters of the artificial neural network to reflect theground-truth-route-accuracy metric. Accordingly, the intelligenttransportation routing system can utilize ground-truth-route-accuracymetric to train the artificial neural network to generate improved routeaccuracy predictions.

The intelligent transportation routing system may also use differenttypes of training data (e.g., measured training data or synthetictraining data) to train an artificial neural network. For instance, insome embodiments, the intelligent transportation routing system receivesGPS locations detected by a client device along a training route. Theintelligent transportation routing system then uses the detected GPSlocations to create regions along the training route and generatetraining route tiles for the regions. In some embodiments, theintelligent transportation routing system simulates route locations forthe training route and then transforms the simulated route locationsinto simulated training GPS locations. By simulating the training GPSlocations along a training route, the intelligent transportation routingsystem can generate and utilize synthetic route tiles as training routetiles.

Having trained the artificial neural network, in certain embodiments,the intelligent transportation routing system applies the trainedartificial neural network to new routes corresponding to new GPSlocations and new map-matched data. For example, the transportationrouting system can generate route tiles for regions along a new route.After generating route tiles, the transportation routing system canprovide the route tiles as input into the trained artificial neuralnetwork. The transportation routing system can utilize the trainedartificial neural network to analyze the route tiles and determineroute-accuracy metrics for the route tiles. By determining aroute-accuracy metric for multiple regions along the new route, theintelligent transportation routing system creates a basis for evaluatingthe entire new route.

For example, in some embodiments, the intelligent transportation routingsystem determines a noise level for subsets of GPS locationscorresponding to multiple regions. The transportation routing system canthen determine more accurate distances and/or location measures for theregions. For example, the intelligent transportation routing system candetermine a more accurate route distance by relying on GPS locations inlow-noise regions and relying on map-matched data in high-noise regions.Similarly, the intelligent transportation routing system can determine amore accurate location of the client device along the route within anyparticular region.

In addition to determining distances or locations, in certainembodiments, the intelligent transportation routing system can alsomodify digital maps to more accurately reflect location data. Forexample, in certain embodiments, the intelligent transportation routingsystem may determine that route-accuracy metrics for multiple routetiles indicate inaccuracies in underlying digital maps. Based on suchobservations, in certain embodiments, the intelligent transportationrouting system adjusts the digital map for a region to correspond to asubset of GPS locations.

The disclosed intelligent transportation routing system overcomesseveral technical deficiencies that hinder conventional transportationrouting systems. For example, the intelligent transportation routingsystem more accurately estimates distances traveled by (or locations of)a client device associated with a transportation vehicle. By using anartificial neural network to determine route-accuracy metrics for routetiles, the intelligent transportation routing system can determinewhether to rely on GPS locations or map-matched locations for particularregions along a route. Accordingly, the disclosed intelligenttransportation routing system can determine when relatively clean GPSlocations (or map-matched locations) deliver a more accuraterepresentation of the route.

In addition to providing a more accurate evaluation of routes, incertain embodiments, the intelligent transportation routing systemimproves flexibility of computing systems evaluating transportationroutes. As suggested above, the intelligent transportation routingsystem can use route tiles and corresponding route-accuracy metrics torely on different location data sources within different regions. Ratherthan relying solely on map-matched locations, the intelligenttransportation routing system exploits an artificial neural network toindicate GPS locations or map-matched locations for a particular regionalong a route. This use of an artificial neural network allows forflexible use of different location data and better route evaluation.

The intelligent transportation routing system's flexibility likewiseenables it to correct digital maps faster than existing transportationrouting systems. Indeed, the intelligent transportation routing systemcan detect differences between GPS locations and map-matched locationsin real time (or near real time). As client devices send GPS locationsfor a region that are inconsistent with map-matched locations for thesame region, the intelligent transportation routing system canexpeditiously adjust a digital map to reflect the GPS locations.

Turning now to the figures, FIG. 1 illustrates a schematic diagram of anenvironment 100 for implementing an intelligent transportation routingsystem 104 in accordance with one or more embodiments. This disclosureprovides an overview of the intelligent transportation routing system104 with reference to FIG. 1. After providing an overview, thedisclosure describes components and processes of the intelligenttransportation routing system 104 in further detail with reference tosubsequent figures.

As shown in FIG. 1, the environment 100 includes server(s) 102, arequestor client device 110, a provider client device 116, and a network108. The requestor client device 110 is associated with a requestor 114,and the provider client device 116 is associated with a provider 120.Both the requestor client device 110 and the provider client device 116are associated with a transportation vehicle 122. As FIG. 1 suggests,the provider 120 provides transportation to the requestor 114 using thetransportation vehicle 122.

As further shown in FIG. 1, the server(s) 102 include an intelligenttransportation routing system 104 and a transportation routing database106. The intelligent transportation routing system 104 utilizes thenetwork 108 to communicate through the server(s) 102 with the requestorclient device 110 and the provider client device 116. For example, theintelligent transportation routing system 104, via the server(s) 102,communicates with the requestor client device 110 and the providerclient device 116 via the network 108 to determine locations of therequestor client device 110 and the provider client device 116. Perdevice settings, for instance, the intelligent transportation routingsystem 104 can utilize the server(s) 102 to receive GPS locations fromthe requestor client device 110 or the provider client device 116 toestimate a route traveled by the requestor client device 110 or theprovider client device 116, respectively.

As used in this disclosure, the term “GPS location” refers to dataindicating a computing device's location using a Global PositioningSystem (or similar system, such as Global System for MobileCommunications). For example, a GPS location includes altitudinal,longitudinal, and/or latitudinal coordinates or degrees that therequestor client device 110 or the provider client device 116 sends tothe server(s) 102 and the intelligent transportation routing system 104.In some instances, the intelligent transportation routing system 104receives data from such a client device encoded to represent a GPSlocation in various standard GPS formats, such as degrees, minutes, andseconds (“DMS”); degrees and decimal minutes (“DMM”), or decimal degrees(“DD”).

As suggested above, GPS locations may indicate a route traveled by therequestor client device 110 or the provider client device 116. The term“route” refers to a course traveled from one location to anotherlocation. In particular, a route includes a course traveled by a clientdevice from a starting location to a destination location. For example,a route may include a course traveled by the requestor client device 110in the transportation vehicle 122 while the transportation vehicle 122transports the requestor 114 from a pickup location to a destinationlocation. As another example, a route may include a course traveled bythe provider client device 116 in the transportation vehicle 122 afterthe provider 120 drops off the requestor 114 and travels to a differentlocation.

As suggested above, the term “requestor” refers to person who requests aride or other form of transportation from the intelligent transportationrouting system 104. A requestor may refer to a person who requests aride or other form of transportation but who is still waiting forpickup. A requestor may also refer to a person whom a transportationvehicle has picked up and who is currently riding within thetransportation vehicle to a destination (e.g., a destination indicatedby a requestor).

Relatedly, the term “requestor client device” refers to a computingdevice associated with (or used by) a requestor. A requestor clientdevice includes a mobile device, such as a laptop, smartphone, or tabletassociated with a requestor. But the requestor client device 110 mayalso be any type of computing device as further explained below withreference to FIG. 13. The requestor client device 110 includes arequestor application 112. In some embodiments, the requestorapplication 112 comprise a web browser, applet, or other softwareapplication (e.g., native application) available to the requestor clientdevice 110.

A requestor may interact with a requestor application to requesttransportation services, receive a price estimate for the transportationservice, and access other transportation-related services. For example,the requestor 114 may interact with the requestor client device 110through graphical user interfaces of the requestor application 112 toinput a pickup location or a destination location for transportation.The intelligent transportation routing system 104, via the server(s)102, in turn provides the requestor client device 110 with a priceestimate for the transportation and an estimated time of arrival of theprovider 120 (or the transportation vehicle 122) through the requestorapplication 112. Having received the price estimate and estimated timeof arrival, the requestor 114 may then select (and the requestor clientdevice 110 detect) a transportation-request option to requesttransportation services from the intelligent transportation routingsystem 104.

Relatedly, the term “provider” refers to a driver or other person whooperates a transportation vehicle and/or who interacts with a providerclient device. For instance, a provider includes a person who drives atransportation vehicle along various routes to pick up and drop offrequestors. As shown in FIG. 1, in certain embodiments, thetransportation vehicle 122 includes or is associated with a provider.However, in other embodiments, the transportation vehicle 122 does notinclude a provider, but is instead an autonomous transportationvehicle—that is, a self-driving vehicle that includes computercomponents and accompanying sensors for driving without manual-providerinput from a human operator.

As noted above, both the provider 120 and the transportation vehicle 122are associated with the provider client device 116. The term “providerclient device” refers to a computing device associated with a provideror a transportation vehicle. The provider client device 116 may beseparate or integral to the transportation vehicle 122. For example, theprovider client device 116 may refer to a separate mobile device, suchas a laptop, smartphone, or tablet associated with the transportationvehicle 122. But the provider client device 116 may be any type ofcomputing device as further explained below with reference to FIG. 13.Additionally, or alternatively, the provider client device 116 may be asubcomponent of a transportation vehicle. Regardless of its form, theprovider client device 116 may include various sensors, such as a GPSlocator, an inertial measurement unit, an accelerometer, a gyroscope, amagnetometer, and/or other sensors that the intelligent transportationrouting system 104 can access to obtain information, such as GPSlocations and other location information.

Relatedly, the term “transportation vehicle” refers to a vehicle thattransports one or more persons for an intelligent transportation routingsystem, such as an airplane, boat, automobile, motorcycle, or othervehicle. This disclosure primarily describes transportation vehicles asautomobiles, such as cars, mopeds, shuttles, or sport utility vehicles,but the intelligent transportation routing system 104 may use any othertransportation vehicle.

Although this disclosure often describes a transportation vehicle asperforming certain functions, the transportation vehicle includes anassociated provider client device that often performs a correspondingfunction. For example, when the intelligent transportation routingsystem 104 sends a transportation-request notification to thetransportation vehicle 122—or queries location information from thetransportation vehicle 122—the intelligent transportation routing system104 sends the transportation-request notification or location query tothe provider client device 116.

As further shown in FIG. 1, the provider client device 116 includes aprovider application 118. In some embodiments, the provider application118 comprises a web browser, applet, or other software application(e.g., native application) available to the provider client device 116.In some instances, the intelligent transportation routing system 104provides data packets including instructions that, when executed by theprovider client device 116, create or otherwise integrate the providerapplication 118 within an application or webpage.

In some embodiments, the intelligent transportation routing system 104communicates with the provider client device 116 through the providerapplication 118. In addition to those communications, the providerapplication 118 optionally includes computer-executable instructionsthat, when executed by the provider client device 116, cause theprovider client device 116 to perform certain functions. For instance,the provider application 118 can cause the provider client device 116 tocommunicate with the intelligent transportation routing system 104 tosend data representing GPS locations or receive a transportation-requestnotification or navigate to a pickup location to pick up a requestor.

As shown in FIG. 1, the intelligent transportation routing system 104receives communications via the server(s) 102. In certain embodiments,the server(s) 102 comprise a content server. The server(s) 102 can alsocomprise a communication server or a web-hosting server. This disclosuredescribes additional details regarding the server(s) 102 below withrespect to FIG. 13. Although not illustrated in FIG. 1, in someembodiments, the environment 100 may have a different arrangement ofcomponents and/or may have a different number or set of componentsaltogether. For example, in some embodiments, the intelligenttransportation routing system 104 and the provider client device 116 cancommunicate directly, bypassing the network 108.

In addition to communicating with the provider client device 116 toreceive GPS locations, the intelligent transportation routing system 104optionally stores data corresponding to each route traveled and eachtransportation request on a transportation routing database 106 accessedby the server(s) 102. Accordingly, the server(s) 102 may generate,store, receive, and transmit various types of data, including, but notlimited to, GPS locations, map-matched locations, location information,price estimates, estimated times of arrival, pickup locations, dropofflocations, and other data stored in the transportation routing database106. In some such embodiments, the intelligent transportation routingsystem 104 organizes and stores such data in the transportation routingdatabase 106 by geographic area, requestor, and/or time period.

As suggested above, when training an artificial neural network, theintelligent transportation routing system 104 identifies both trainingGPS locations and training map-matched locations for a training routetraveled by a client device associated with the transportation vehicle122. As indicated by FIG. 1, the client device may be the requestorclient device 110 and/or the provider client device 116. This disclosureuses the term “training” for training GPS locations, trainingmap-matched locations, training routes, training route tiles, and other“training” objects to indicate that the intelligent transportationrouting system 104 uses those objects to train an artificial neuralnetwork.

As used in this disclosure, the term “map-matched location” refers to alocation on a digital map corresponding to a GPS location. For example,map-matched locations may correspond to locations along a road within adigital map that correspond to GPS locations received from a clientdevice. In certain embodiments, the intelligent transportation routingsystem 104 estimates locations along roads of a digital map based on GPSlocations. For instance, the intelligent transportation routing system104 may use hidden Markov models, path-inference filter, or otherexisting map-matching processes to infer locations along a road within adigital map based on GPS locations. Additionally, the intelligenttransportation routing system 104 may access digital maps from thetransportation routing database 106 (or from a third-party server) onwhich to map-match the GPS locations, such as Open Street Maps or otheravailable digital maps. As suggested above, the intelligenttransportation routing system 104 may use a set of training map-matchedlocations and training GPS locations corresponding to a training routeto train an artificial neural network.

After identifying training GPS locations and training map-matchedlocations for a training route, in certain embodiments, the intelligenttransportation routing system 104 generates regions along the trainingroute. As used in this disclosure, the term “region” refers to ageographical space that includes or surrounds portions of an estimatedroute. For example, in certain embodiments, a region includes arectangular space that surrounds a portion of an estimated route basedon GPS locations and/or map-matched locations. The rectangular space maybe, for instance, a squared region measured in centimeters, inches,feet, meters, or some other measurement.

When the intelligent transportation routing system 104 generatesregions, in some embodiments, a region includes a subset of training GPSlocations and a subset of training map-matched locations. Based on thesubset of training GPS locations and the subset of training map-matchedlocations, the intelligent transportation routing system 104 generates atraining route tile for the region. FIG. 3A depicts examples of suchregions.

As used in this disclosure, the term “route tile” refers to a digitalrepresentation of GPS locations and/or map-matched locations within aregion along a route. A route tile may represent GPS locations and/ormap-matched locations (for a region) in a single route tile or inmultiple route tiles. For example, in certain embodiments, a route tileincludes two image matrices. A first image matrix may includerepresentations of a subset of GPS locations corresponding to a region.A second image matrix may include representations of a subset ofmap-matched locations corresponding to the region. FIG. 3B depictsexamples of a route tile. As suggested above, in some embodiments, theintelligent transportation routing system 104 generates training routetiles corresponding to regions along a training route to train anartificial neural network.

In addition to generating a training route tile for a region, theintelligent transportation routing system 104 uses an artificial neuralnetwork to predict a route-accuracy metric for the region based on thetraining route tile. The term “route-accuracy metric” refers to anindicator of GPS locations or map-matched locations for a region. Inparticular, the term “route-accuracy metric” includes an indicator ofaccuracy, precision, or reliability of a subset of GPS locations ormap-matched locations for a region. For example, the term“route-accuracy metric” includes an indicator of whether GPS locationsor map-matched locations more accurately represent a route traveled by aclient device.

A route-accuracy metric may take different forms. For example, in someimplementations, a route-accuracy metric may be an accuracy classifierthat indicates a noise level for a subset of GPS locations correspondingto a region along the route (e.g., whether the GPS locations are noisyor clean or whether the system should rely on the GPS locations or themap-matched locations). As another example, in some cases, aroute-accuracy metric may be an indicator of the regional distancetraveled by a client device within a region based on a route tile (e.g.,a predicted regional distance as opposed to the distance of the routeindicated by the GPS locations or the map-matched locations).Alternatively, a route-accuracy metric may be an estimated path traveledby the client device within a region along the route (e.g., a predictedpath as opposed to the path indicated by the GPS locations or themap-matched locations).

Relatedly, the term “ground-truth-route-accuracy metric” refers to aroute-accuracy metric based on empirical observation (or simulatedobservation) utilized to determine error of a predicted route accuracymetric. In particular, the term “ground-truth-route-accuracy metric”includes a route-accuracy metric that is provided to an artificialneural network to determine an error or loss of a predictedroute-accuracy metric. For example, a ground-truth-route-accuracy metricmay include an accurate (i.e. ground-truth) classification label,distance, or path for a particular region. In certain embodiments, theintelligent transportation routing system 104 compares a predictedroute-accuracy metric to a ground-truth-route-accuracy metric for thesame region. Based on a comparison between theground-truth-route-accuracy metric and a predicted route-accuracymetric, the intelligent transportation routing system 104 adjustsparameters of the artificial neural network to reflect theground-truth-route-accuracy metric. Such adjustments train theartificial neural network to improve the accuracy of its predictedroute-accuracy metrics for training route tiles.

The term “artificial neural network” refers to a machine learning modelthat can be tuned (e.g., trained) based on training input to approximateunknown functions. In particular, the term “artificial neural network”can include a model of interconnected digital neurons that communicateand learn to approximate complex functions and generate outputs based ona plurality of inputs provided to the model. For instance, the term“artificial neural network” includes convolutional neural networks andfully convolutional neural networks. Artificial neural networks alsoinclude feedforward neural networks or recurrent neural networks. Anartificial neural network includes an algorithm that implements deeplearning techniques, i.e., machine learning that utilizes a set ofalgorithms to attempt to model high-level abstractions in data.

In some embodiments, the intelligent transportation routing system 104trains or uses a convolutional neural network as its artificial neuralnetwork. As used in this disclosure, the term “convolutional neuralnetwork” refers to an artificial neural network that uses one or moreconvolutional layers to analyze features extracted from an input.Additional detail regarding architecture of a convolutional neuralnetwork is provided below.

In addition to training the artificial neural network, in certainembodiments, the intelligent transportation routing system 104 uses theartificial neural network to analyze route tiles corresponding to aroute traveled by a client device and to determine route-accuracymetrics for regions along the route. This disclosure describes theapplication process of a trained artificial neural network below.

Although FIG. 1 illustrates the intelligent transportation routingsystem 104 as part of the server(s) 102, in one or more embodiments, theintelligent transportation routing system 104 is implemented, in wholeor in part, by other computing devices within the environment 100. Forexample, in one or more embodiments, the intelligent transportationrouting system 104 is implemented as part of the requestor client device110 (e.g., as part of the requestor application 112). Moreover, in someembodiments, the intelligent transportation routing system 104 isimplemented as part of the provider client device 116 (e.g., as part ofthe provider application 118). Accordingly, the intelligenttransportation routing system 104 can utilize the server(s) 140, therequestor client device 110, and the provider client device 116 toarrange train and implement the artificial neural network.

As noted above, the intelligent transportation routing system 104identifies locations for a route traveled by a client device. FIGS.2A-2C illustrate an example of such locations and a route—including GPSlocations 202 corresponding to a route, map-matched locations 204corresponding to the route, and a ground-truth representation 208 of theroute. As FIGS. 2A-2C indicate, the GPS locations 202, the map-matchedlocations 204, and the ground-truth representation 208 each indicatedifferent versions of a route traveled by a client device. Thedifferences between the GPS locations 202 and the map-matched locations204 reflect the inaccuracies of determining a route based on GPS ormap-matching data. To eliminate some of these inaccuracies, in certainembodiments, the intelligent transportation routing system 104determines whether GPS locations or map-matched locations moreaccurately represent the route traveled by a client device.

As shown in FIG. 2A, the intelligent transportation routing system 104receives GPS data corresponding to the GPS locations 202 from a clientdevice (e.g., the provider client device 116 and/or the requestor clientdevice 110). In this particular embodiment, a subset of GPS locations202 a correspond to clean GPS data. The GPS data corresponding to thesubset of GPS locations 202 a may be clean because a GPS receiver forthe client device transmits, propagates, and/or receives a GPS signalfor the subset with relatively little distortion. By contrast, a subsetof GPS locations 202 b corresponds to noisy GPS data. The GPS datacorresponding to the subset of GPS locations 202 b may be noisy becausethe GPS receiver for the client device transmits, propagates, and/orreceives a GPS signal for the subset with relatively high distortion.Because the underlying GPS data is noisy, the subset of GPS locations202 b does not accurately represent the locations of the client devicealong the route.

Although FIG. 2A depicts the GPS locations 202 as individual locations,FIG. 2B depicts the map-matched locations 204 as a contiguous path. Byshowing the map-matched locations 204 as a contiguous path, themap-matched locations 204 include interpolated straight lines betweensuccessive map-matched locations. In other embodiments, however, themap-matched locations 204 may be represented as individual locations.

As indicated by FIGS. 2A and 2B, the GPS locations 202 and themap-matched locations 204 indicate different estimated routes traveledby the client device. The map-matching process explains in part thedifferent contours of these estimated routes. In the embodiment shown inFIGS. 2A and 2B, the intelligent transportation routing system 104 useshidden Markov models to infer locations along a road 206 within adigital map from the GPS locations 202. Based on these inferences, theintelligent transportation routing system 104 “snaps” or adjusts the GPSlocations 202 to the map-matched locations 204. As shown in FIG. 2B, themap-matched locations 204 closely track the road 206 within a digitalmap. For instance, subsets of map-matched locations 204 a and 204 bclosely track different contours of the road 206.

As suggested above, certain map-matching processes rest on an assumptionthat the roads of a digital map accurately reflect current roadlocations. Accordingly, if a digital map's road accurately reflectsconditions on the ground, map-matched locations may more accuratelyrepresent a route than corresponding GPS locations. By contrast, if adigital map's road does not accurately reflect conditions on the ground,the map-matched locations may not more accurately reflect the route thancorresponding GPS locations.

In addition to the road 206 from a digital map depicted in FIG. 2B, FIG.2C depicts a ground-truth road 210. The ground-truth road 210 representsthe road upon which the transportation vehicle 122 associated with theclient device actually traveled. As shown in FIGS. 2B, 2C, the road 206corresponding to the map-matched locations 204 differs from theground-truth road 210 in certain respects. The ground-truthrepresentation 208 of the route tracks the contours of the ground-truthroad 210. As noted above, in some embodiments, the intelligenttransportation routing system 104 determines whether portions of the GPSlocations 202 or portions of the map-matched locations 204 moreaccurately represent the ground-truth representation 208 of theroute—that is, the route actually traveled by the client device.

For example, the intelligent transportation routing system 104 maycompare the subsets of GPS locations 202 a and 202 b to thecorresponding subsets of map-matched locations 204 a and 204 b,respectively, to determine which of the location subsets to use for aparticular region. As shown in FIGS. 2A-2C, the subset of GPS locations202 a more accurately reflects the ground-truth representation 208 ofthe route than the corresponding subset of map-matched locations 204 a.By contrast, the subset of GPS locations 202 b more accurately reflectsthe ground-truth representation 208 of the route than the correspondingsubset of map-matched locations 204 b. This disclosure describesembodiments of the intelligent transportation routing system 104 thatanalyze such location subsets (and how to distinguish between them).

As suggested above, in some embodiments, the intelligent transportationrouting system 104 analyzes location subsets according to region usingroute tiles. FIGS. 3A-3B depict the intelligent transportation routingsystem 104 both analyzing regions along a route traveled by a clientdevice and generating route tiles corresponding to each region along theroute. As shown in FIG. 3A, for example, the intelligent transportationrouting system 104 segments a trace of the GPS locations 202 intoregions 302 a-316 a. In this particular embodiment, the intelligenttransportation routing system 104 segments the trace of the GPSlocations 202 into squares. As shown in FIG. 3A, each of the regions 302a-316 a represents an l×l square meter, where l represents a length inmeters of one side of the region. In alternative embodiments, theintelligent transportation routing system 104 may use other shapes torepresent regions and/or other measurements of the regions boundaries,such as using circular regions with a radius defined in miles.

As further shown in FIG. 3A, the intelligent transportation routingsystem 104 also segments a trace of the map-matched locations 204 intoregions 302 b-316 b. The regions 302 b-316 b cover the same geographicareas as (and correspond to) the regions 302 a-316 a, respectively.Consequently, the regions 302 a-316 a are the same as the regions 302b-316 b. For purposes of illustration, however, FIG. 3A depicts theregions 302 a-316 a and the regions 302 b-316 b separately. But askilled artisan will recognize that when this disclosure refers to theregion 302 a, it likewise refers to the region 302 b, and so on and soforth. As shown separately, however, the regions 302 a-316 arespectively include subsets of GPS locations 318 a-318 h (from amongthe GPS locations 202), and the regions 302 b-316 b respectively includesubsets of map-matched locations 320 a-320 h (from among the map-matchedlocations 204).

By segmenting the GPS locations 202 and the map-matched locations 204into the same geographic areas, the intelligent transportation routingsystem 104 facilitates comparison of the subsets of GPS locations 318a-318 h and the subsets of map-matched locations 320 a-320 h. Forexample, in the regions 302 a and 302 b, the subset of GPS locations 318a and the subset of map-matched locations 320 a each represent estimatesof the route traveled by the client device. As described below, theintelligent transportation routing system 104 compares these locationsubsets using a route tile for the regions 302 a and 302 b.

FIG. 3B illustrates an example of a region that includes locationsubsets indicating different estimated routes. As shown in FIG. 3B, thesubset of GPS locations 318 c within the region 306 a indicates adifferent estimated route than the subset of map-matched locations 320 cwithin the region 306 b. The subset of GPS locations 318 c (shown in theregion 306 a) are based on relatively clean GPS data and more accuratelyrepresent the route for the shared region than the subset of map-matchedlocations 320 c (shown in the region 306 b).

After segmenting a trace of GPS locations into regions, in certainembodiments, the intelligent transportation routing system 104 generatesroutes tiles corresponding to each region along a route. As noted above,a route tile includes a digital representation of the GPS locationsand/or map-matched locations for a region along a route. FIG. 3B depictsone such route tile. As shown, the intelligent transportation routingsystem 104 generates a route tile 324 that includes two image matrices—afirst image matrix 322 a and a second image matrix 322 b. The firstimage matrix 322 a includes representations of the subset of GPSlocations 318 c corresponding to the region 306 a. The second imagematrix 322 b includes representations of the subset of map-matchedlocations 320 c corresponding to the region 306 b.

To facilitate creating the route tile 324, in some embodiments, theintelligent transportation routing system 104 estimates a first path ofthe client device within the region 306 a based on the subset of GPSlocations 318 c. Similarly, the intelligent transportation routingsystem 104 estimates a second path of the client device within theregion 306 b based on the subset of map-matched locations 320 c. Theintelligent transportation routing system 104 generates pixels torepresent both estimated paths in the first image matrix 322 a and thesecond image matrix 322 b of the route tile 324 as well as pixels torepresent positions outside of the estimated paths.

As further shown in FIG. 3B, the first image matrix 322 a includes afirst set of pixels that represent the first estimated path of theclient device within the region 306 a. The first image matrix 322 a alsoincludes a second set of pixels that represent positions outside of thefirst estimated path. Similarly, the second image matrix 322 b includesa third set of pixels that represent the second estimated path of theclient device within the region 306 b. The second image matrix 322 balso includes a fourth set of pixels that represent positions outside ofthe second estimated path.

In the embodiment shown in FIG. 3B, the first and third sets of pixelshave one numeric value (shown as ones) to depict an estimated path ofthe client device. By contrast, the second and fourth sets of pixelshave another numeric value (shown as zeros) to depict positions outsidean estimated path of the client device. Continuing the example fromabove using l×l square meters for each region, in certain embodiments,each pixel includes either (a) the number one to represent the presenceof the client device within the region or (b) zero to represent theabsence of the client device. The intelligent transportation routingsystem 104 generates each image matrix by segmenting an estimated pathinto l×l regions and representing the data in an m×m image matrix, wherem=l/g and g represents pixel granularity in meters per pixel.Accordingly, the first image matrix 322 a represents an m×m image matrixfor the first estimated path according to the GPS locations 318 c in theregion 306 a, and the second image matrix 322 b represent an m×m imagematrix for the second estimated path according to the subset ofmap-matched locations 320 c in the region 306 b. Together, the routetile 324 includes pixels with m×m×2 matrices. In certain embodiments,the intelligent transportation routing system 104 generates a route tilewith m×m×2 matrices for each of the regions 302 a-316 a and 302 b-316 b.

Although FIG. 3B illustrates particular pixel values (e.g., 1 or 0), incertain embodiments, the pixels may include different values, such asdifferent color values or byte images. Accordingly, in certainembodiments, image matrices may include a color representation of thepath represented by the subset of GPS locations 318 c and another colorrepresentation of the path represented by the subset of map-matchedlocations 320 c.

Moreover, although FIG. 3B illustrates utilizing a first image matrixbased on GPS locations and a second image matrix based on map-matchedlocations, in one or more embodiments, the intelligent transportationrouting system 104 generates route tiles based on other data. Forexample, rather than utilizing map-matched locations to generate thesecond image matrix 322 b, the intelligent transportation routing system104 can utilize a different image (in addition to or in the alternativeto map-matched locations). In particular, the intelligent transportationrouting system 104 can utilize a satellite image (or full mapinformation, such as center line, curb and gutter, building contours,traffic rules, etc.) to generate the second image matrix 322 b. Toillustrate, the intelligent transportation routing system 104 cansegment a satellite image into the regions 302 a-316 a and generate aroute tile based on the pixels of the satellite image that correspondsto each of the regions 302 a-316 a. Specifically, the intelligenttransportation routing system 104 can generate a second image matrix foreach region based on the pixels of the satellite image that correspondto the region. The intelligent transportation routing system 104 cancombine the second image matrix reflecting the satellite image pixelsfor the region with the first image matrix reflecting the subset of GPslocations for the region to generate a route tile.

Regardless of the format of a route tile, in some embodiments, theintelligent transportation routing system 104 generates training routetiles for each region along a route based on the region's respectivesubset of training GPS locations and subset of training map-matchedlocations. Accordingly, the training route tiles may include imagematrices with pixel sets representing an estimated path according to asubset of training GPS locations and a subset of training map-matchedlocations. Consistent with the disclosure above, the intelligenttransportation routing system 104 generates such training route tilesusing the methods described above. FIG. 4 illustrates the intelligenttransportation routing system 104 training an artificial neural networkto use the training route tiles to predict route-accuracy metrics forregions along a training route. When describing the training process,this disclosure uses the terms “region” and “training region”interchangeably.

As an overview of the training process shown in FIG. 4, the intelligenttransportation routing system 104 iteratively inputs training routetiles 402 a-402 n and corresponding ground-truth-route-accuracy metrics404 a-404 n into an artificial neural network 406. The intelligenttransportation routing system 104 uses the artificial neural network 406to predict route-accuracy metrics 408 a-408 n (one by one) based on thetraining route tiles 402 a-402 n. The artificial neural network 406applies a loss function 410 to determine a calculated loss between thepredicted route-accuracy metrics 408 a-408 n and theground-truth-route-accuracy metrics 404 a-404 n, respectively. Based onthe calculated loss in each iteration, the intelligent transportationrouting system 104 adjusts parameters of the artificial neural network406 to ultimately generate a trained artificial neural network 412.

As part of each iteration, the intelligent transportation routing system104 inputs the training route tiles 402 a-402 n (e.g., one trainingroute tile at a time). Each of the training route tiles 402 a-402 nrespectively corresponds to the ground-truth-route-accuracy metrics 404a-404 n. In certain embodiments, the intelligent transportation routingsystem 104 inputs a training route tile together with a correspondingground-truth-route-accuracy metric. For example, in one iteration, theintelligent transportation routing system 104 inputs the training routetile 402 a and the ground-truth-route-accuracy metric 404 a into theartificial neural network 406. By cycling through multiple iterations,the intelligent transportation routing system 104 uses theground-truth-route-accuracy metrics 404 a-404 n as a label for thetraining route tiles 402 a-402 n to train the artificial neural network406 to accurately predict route-accuracy metrics.

As suggested above, in certain embodiments, theground-truth-route-accuracy metrics 404 a-404 n each indicate either asubset of training GPS locations or a subset of training map-matchedlocations for a given region. In particular, theground-truth-route-accuracy metrics 404 a-404 n indicate whether thesubset of training GPS locations or the subset of training map-matchedlocations are more accurate for a given region reflected in acorresponding training route tile. For example, in some embodiments, theground-truth-route-accuracy metrics 404 a-404 n each indicate a noiselevel for a subset of training GPS locations for a given region. In somesuch implementations, the ground-truth-route-accuracy metrics 404 a-404n comprise accuracy classifiers indicating whether a noise level for asubset of training GPS locations exceeds or falls below a GPS-noisethreshold. Such an accuracy classifier may denote (i) a number one toindicate that a subset of training GPS locations is based on noisy GPSdata or (ii) a zero to indicate that the subset of training GPSlocations is based on clean GPS data.

After inputting a training route tile and a ground-truth-route-accuracymetric, the intelligent transportation routing system 104 uses theartificial neural network 406 to predict a route-accuracy metric for thetraining route tile. Through running multiple iterations, the artificialneural network 406 predicts each of the route-accuracy metrics 408 a-408n for the training route tiles 402 a-402 n, respectively.

To facilitate comparison, the route-accuracy metrics 408 a-408 n takethe same form as the ground-truth-route-accuracy metrics 404 a-404 n.For example, in some embodiments, the route-accuracy metric 408 a is apredicted accuracy classifier for the training route tile 402 aindicating a predicted noise level of a subset of training GPS locations(e.g., noisy or clean). In such embodiments, theground-truth-route-accuracy metric 404 a is similarly an accuracyclassifier for the training route tile 402 a indicating the actualclassification (e.g., noisy or clean) of the subset of training GPSlocations. Both the route-accuracy metrics 408 b-408 n and theground-truth-route-accuracy metrics 404 b-404 n likewise take the sameformat.

As noted above, upon predicting a route-accuracy metric for a trainingroute tile, the intelligent transportation routing system 104 applies aloss function 410 to determine a calculated loss. In particular, theloss function 410 determines a difference between theground-truth-route-accuracy metrics 404 a-404 n and the predictedroute-accuracy metrics 408 a-408 n, respectively. For example, in aniteration of certain embodiments, the intelligent transportation routingsystem 104 applies a cross-entropy-loss function to compare theroute-accuracy metric 408 a to the ground-truth-route-accuracy metric404 a. In such embodiments, the loss function 410 determines crossentropy loss between the route-accuracy metric 408 a and theground-truth-route-accuracy metric 404 a.

As further shown in FIG. 4, the intelligent transportation routingsystem 104 uses the calculated loss from the loss function 410 to trainthe artificial neural network 406 to more accurately predictroute-accuracy metrics (e.g., accuracy classifiers indicating noiselevels). In particular, the intelligent transportation routing system104 provides the calculated loss to the artificial neural network 406 ineach iteration. The intelligent transportation routing system 104 thenadjusts parameters of the artificial neural network 406 to minimize thecalculated loss and more accurately reflect theground-truth-route-accuracy metric for a given iteration. Throughmultiple iterations, the intelligent transportation routing system 104uses the artificial neural network 406 to repeatedly predictroute-accuracy metrics for training route tiles and adjusts theparameters of the artificial neural network 406 to generate the trainedartificial neural network 412. In some such embodiments, the intelligenttransportation routing system 104 trains the artificial neural network406 using thousands (or millions) of training route tiles in discretebatches (e.g., 1.5 million training route tiles in batches of 250).

The disclosure above describes FIG. 4 in terms of accuracy classifiersfor the ground-truth-route-accuracy metrics 404 a-404 n and theroute-accuracy metrics 408 a-408 n. In alternative embodiments, theintelligent transportation routing system 104 uses differentroute-accuracy metrics to train the artificial neural network 406. Forexample, in certain embodiments, the ground-truth-route-accuracy metricand the route-accuracy metric respectively comprise a regional distancetraveled by a client device within a region along a route and apredicted regional distance traveled by the client device within theregion along the route. Alternatively, the ground-truth-route-accuracymetric and the route-accuracy metric respectively comprise a pathtraveled by a client device within a region along the route and apredicted path traveled by the client device within the region along theroute. In this manner, the intelligent transportation routing system 104can train the artificial neural network 406 to predict an actualdistance or actual path traveled by the client device.

As indicated above, in some such embodiments, the artificial neuralnetwork 406 comprises a convolutional neural network. Alternatively, theintelligent transportation routing system 104 uses a feedforward neuralnetwork to solve a regression problem. By solving a regression problem,the feedforward neural network can, in certain embodiments, predictregional distances or estimated paths traveled by a client device basedon training route tiles.

FIG. 5 illustrates one exemplary convolutional neural network inaccordance with one or more embodiments of the intelligenttransportation routing system 104. In particular, FIG. 5 shows aconvolutional neural network 500 that includes convolutional layers,max-pooling layers, fully-connected layers, and a dropout layer. Theconvolutional neural network 500 includes such layers in the followingorder: a first convolutional layer 504 a, a first max-pooling layer 506a, a second convolutional layer 504 b, a second max-pooling layer 506 b,a first fully-connected layer 508 a, a dropout layer 510, and a secondfully-connected layer 508 b.

As further shown in FIG. 5, the first convolutional layer 504 a receivesan input 502 from the intelligent transportation routing system 104.When training the convolutional neural network 500, the input 502comprises a training route tile and a correspondingground-truth-route-accuracy metric in each iteration. By contrast, whenapplying a trained version of the convolutional neural network 500 todetermine route-accuracy metrics, the input 502 comprises a route tile.Alternatively, a trained convolutional neural network 500 may receive aroute tile together with a ground-truth-route-accuracy metric having anull value as the input 502. In applying the trained artificial neuralnetwork (e.g., with an input route tile and null label) no update isperformed on the parameters within the artificial neural network(because the trained artificial neural network is not being trainedbased on a ground-truth value).

In addition to the input 502, the convolutional neural network 500includes rectified linear units (“ReLUs”) 512 a, 512 b, and 512 c inbetween certain layers. As shown in FIG. 5, the convolutional neuralnetwork 500 uses the ReLU 512 a to apply a rectifier to an output of thefirst convolutional layer 504 a, the ReLU 512 b to apply a rectifier tothe output of the second convolutional layer 504 b, and the ReLU 512 cto apply a rectifier to the output of the first fully-connected layer508 a.

As shown at the end of the convolutional neural network 500, the secondfully-connected layer 508 b generates an output 514. When training theconvolutional neural network 500, the output 514 for each iterationcomprises a predicted route-accuracy metric for the training route tileinput by the intelligent transportation routing system 104. By contrast,when applying a trained version of the convolutional neural network 500,the output 514 for each iteration comprises a determined route-accuracymetric for the route tile input by the intelligent transportationrouting system 104.

In addition to training an artificial neural network, the intelligenttransportation routing system 104 also applies a trained artificialneural network to route tiles to determine route-accuracy metrics. FIG.6 illustrates an embodiment of the intelligent transportation routingsystem 104 applying a trained artificial neural network. As an overviewof FIG. 6, the intelligent transportation routing system 104 identifiesGPS locations 602 and map-matched locations 604—both corresponding to aroute traveled by a client device associated with a transportationvehicle. The intelligent transportation routing system 104 thengenerates (i) regions along the route and (ii) route tiles 606 a-606 nfor the regions based on subsets of GPS locations 602 a-602 n andsubsets of map-matched locations 604 a-604 n. The intelligenttransportation routing system 104 subsequently uses the artificialneural network 608 to determine route-accuracy metrics 610 a-610 n basedon the route tiles 606 a-606 n. Having determined the route-accuracymetrics 610 a-610 n, the intelligent transportation routing system 104performs the act 612 of updating the route (e.g., by determining adistance of the route).

When identifying the GPS locations 602, in certain embodiments, theintelligent transportation routing system 104 receives the GPS locations602 detected by a client device traveling along a route. For example, insome implementations, a GPS receiver of the client device receives GPSdata representing the GPS locations 602. The client device thentransmits the GPS data representing the GPS locations 602 to theintelligent transportation routing system 104.

After receiving the GPS locations 602, in certain embodiments, theintelligent transportation routing system 104 uses a map-matchingprocess to infer locations along a road within a digital map based onthe GPS locations 602. By inferring locations along the road, theintelligent transportation routing system 104 generates the map-matchedlocations 604. In some embodiments, the intelligent transportationrouting system 104 communicates with third-party server(s) (not shown)that perform a map-matching process to adjust the GPS locations 602 tocreate the map-matched locations 604.

Having identified the GPS locations 602 and the map-matched locations604, the intelligent transportation routing system 104 generates regionsalong the route. Consistent with the disclosure above, in certainimplementations, the intelligent transportation routing system 104segments a trace of the GPS locations 602 into regions. The intelligenttransportation routing system 104 likewise segments a trace of themap-matched locations 604 into the same regions. Accordingly, each ofthe regions along the route includes one of the subsets of GPS locations602 a-602 n and one of the subsets of map-matched locations 604 a-604 n,respectively.

In addition to creating regions of location subsets, the intelligenttransportation routing system 104 generates the route tiles 606 a-606 n.As an example, in certain embodiments, the route tile 606 a includes (i)a representation of the subset of GPS locations 602 a and (ii) arepresentation of the subset of map-matched locations 604 a. Each of theroute tiles 606 b-606 n similarly include (i) a representation of one ofthe subsets of GPS locations 602 b-602 n, respectively, and (ii) arepresentation of one of the subsets of map-matched locations 604 b-602n, respectively.

Upon receiving a route tile, the artificial neural network 608determines a route-accuracy metric for each region. In some suchembodiments, the artificial neural network 608 determines aroute-accuracy metric for the route tile corresponding to each region(e.g., the route tiles 606 a-606 n). The route-accuracy metrics 610a-610 n may take the form of any of the route-accuracy metrics describedabove, including those described with reference to FIG. 4—except thatthe route tiles would not be used for training for the embodimentsdepicted in FIG. 6. For example, in some embodiments, the route-accuracymetric 610 a is an accuracy classifier for the route tile 606 aindicating a noise level of the subset of GPS locations 602 a.Additionally, or alternatively, in certain embodiments, theroute-accuracy metric 610 a indicates either the subset of GPS locations602 a or the subset of map-matched locations 604 a. In some suchembodiments, the route-accuracy metric 610 a indicates which of thesubset of GPS locations 602 a or the subset of map-matched locations 604a more accurately represents the route traveled by the client device.

Based on a route-accuracy metric, in certain embodiments, theintelligent transportation routing system 104 selects one of the subsetof GPS locations 602 a or the subset of map-matched locations 604 a. Theintelligent transportation routing system 104 makes a similar selectionof one of the subset of GPS locations or one of the subset ofmap-matched locations for each region corresponding to the route tiles606 b-606 n. Such a route-accuracy metric may include, but is notlimited to, an accuracy classifier for a route tile indicating a noiselevel of the subset of GPS locations, an indication of either the subsetof GPS locations or the subset of map-matched locations, or anindication of which of the subset of GPS locations or the subset ofmap-matched locations more accurately represents the route. For example,in some embodiments, the intelligent transportation routing system 104selects the subset of GPS locations 602 a for a corresponding regionbased on an accuracy classifier for the route tile 606 a indicating thata noise level of the subset of GPS locations 602 a falls below aGPS-noise threshold. As another example, in certain implementations, theintelligent transportation routing system 104 selects the subset ofmap-matched locations 604 a for a corresponding region based on anaccuracy classifier for the route tile 606 a indicating that a noiselevel of the subset of GPS locations 602 a exceeds a GPS-noisethreshold. Additional examples of selecting a subset of GPS locations ora subset of map-matched locations are provided below within the contextof updating the route.

As further shown in FIG. 6, the intelligent transportation routingsystem 104 performs the act 612 of updating the route. In some suchembodiments, the intelligent transportation routing system 104 updatesthe route to include a selected subset of GPS locations or a subset ofmap-matched locations for each region. The intelligent transportationrouting system 104 can utilize the route-accuracy metrics 408 a-408 n toupdate the route in several ways. For instance, in certain embodiments,the intelligent transportation routing system 104 determines a distanceof the route traveled by the client device based on the route-accuracymetrics 610 a-610 n. In some such embodiments, the intelligenttransportation routing system 104 uses route-accuracy metrics todetermine (for each region) whether to indicate (i) a subset of GPSlocations corresponding to the region or (ii) a subset of map-matchedlocations corresponding to the region. Upon making such a determination,the intelligent transportation routing system 104 uses the indicatedlocation subset when determining a distance of the route.

For example, in certain implementations, the intelligent transportationrouting system 104 determines individual distances between each of GPSlocations within a subset of GPS locations or between individualdistances between each of the map-matched locations within a subset ofmap-matched locations. The intelligent transportation routing system 104then sums the individual distances to determine a regional distancetraveled by the client device within a given region.

In some such embodiments, the route-accuracy metric 610 a comprises anaccuracy classifier indicating that a noise level of the subset of GPSlocations 602 a falls below a GPS-noise threshold. When a noise levelfalls below a GPS-noise threshold, in certain implementations, theintelligent transportation routing system 104 selects the correspondingsubset of GPS locations to determine a regional distance for the region.By contrast, when a noise level exceeds the GPS-noise threshold, in someembodiments, the intelligent transportation routing system 104 selectsthe corresponding subset of map-matched locations to determine aregional distance for the region. Because the noise level of the subsetof GPS locations 602 a falls below a GPS-noise threshold in the exampleabove, the intelligent transportation routing system 104 determines aregional distance traveled by the client device within the region(corresponding to the route tile 606 a) based on the subset of GPSlocations 602 a. The intelligent transportation routing system 104 maysimilarly determine (for each remaining region along the route) whetherto indicate one of the subsets of GPS locations 602 b-602 n or one ofthe subsets of map-matched locations 604 b-604 n. After making suchdeterminations, the intelligent transportation routing system 104 sumsup the regional distance for each region along the route to determine adistance of the route.

Alternatively, in certain embodiments, a route-accuracy metric comprisesa regional distance traveled by the client device within the regioncorresponding to a route tile. Take the route-accuracy metric 610 aagain for example. In some such embodiments, the intelligenttransportation routing system 104 may train the artificial neuralnetwork 608 to determine the regional distance traveled by the clientdevice within the region corresponding to the route tile 606 a. Afterdetermining the regional distance for each remaining region along theroute, the intelligent transportation routing system 104 sums up theregional distance for each region to determine a distance of the route.

In addition (or in the alternative) to determining distance, in certainembodiments, the intelligent transportation routing system 104determines a location of the client device based on one of theroute-accuracy metrics 610 a-610 n. In some such embodiments, theintelligent transportation routing system 104 uses the route-accuracymetric 610 b to determine (for the region corresponding to the routetile 606 b) whether to indicate (i) the subset of GPS locations 602 bcorresponding to the region or (ii) the subset of map-matched locations604 b corresponding to the region. The region corresponding to the routetile 606 b may be, for example, a last region along the route. Upondetermining which location subset to use for the corresponding region,the intelligent transportation routing system 104 estimates a pathtraveled within the region to determine a location of the client device.Alternatively, the intelligent transportation routing system 104identifies an individual GPS location from the subset of GPS locations602 b to determine a location of the client device within thecorresponding region at a given time. The intelligent transportationrouting system 104 may likewise identify an individual map-matchedlocation from the subset of map-matched locations 604 b to determine alocation of the client device. Although the foregoing examples use theroute-accuracy metric 610 b and the route tile 606 b as an example, theintelligent transportation routing system 104 may similarly determine alocation of the client device based on any one of the route-accuracymetrics 610 a-610 n and the route tiles 606 a-606 n.

Alternatively, in certain embodiments, the route-accuracy metric 610 bcomprises an estimated path traveled by the client device within theregion corresponding to the route tile 606 b. In some such embodiments,the intelligent transportation routing system 104 may train theartificial neural network 608 to determine the estimated path traveledby the client device within the region corresponding to the route tile606 b. After determining the estimated path, the intelligenttransportation routing system 104 identifies a location of the clientdevice within the corresponding region at a given time.

In addition (or in the alternative) to determining a client device'slocation, in some embodiments, the intelligent transportation routingsystem 104 uses the route-accuracy metrics 610 a-610 n to modify digitalmaps to more accurately reflect location data. For example, in certainembodiments, the intelligent transportation routing system 104 maydetermine that route-accuracy metrics for route tiles corresponding to agiven region indicate inaccuracies in underlying digital maps. The routetiles for the given region may come from multiple routes traveled bydifferent client devices within the region.

To illustrate, in some such embodiments, the intelligent transportationrouting system 104 utilizes a threshold before modifying a digital map.For example, the intelligent transportation routing system 104determines that a number of route-accuracy metrics for a particularregion indicate GPS locations are more accurate than map-matchedlocations. If the number of route-accuracy metrics satisfies thethreshold, the intelligent transportation routing system 104 can modifythe underlying map based on detected GPS locations.

Similarly, the intelligent transportation routing system 104 maydetermine that route-accuracy metrics for route tiles for a regionindicate (i) a threshold number of subsets of GPS locationscorresponding to a region are relatively clean (e.g., based on aGPS-noise threshold) and (ii) the subset of GPS locations correspondingto the region indicate a path that differs from a map-matched roadwithin a digital map. Based on this determination, the intelligenttransportation routing system 104 can modify the underlying digital map.

As just mentioned, in certain embodiments, the intelligenttransportation routing system 104 adjusts the digital map for the regionto correspond to a subset of GPS locations. For example, the intelligenttransportation routing system 104 may adjust a road's contour within thedigital map to correspond to a contour indicated by the subsets of GPSlocations corresponding to the region. Alternatively, the intelligenttransportation routing system 104 may alter a location of a road withinthe digital map to correspond to a road indicated by the subsets of GPSlocations (e.g., by removing the road within the digital map andcreating a new road based on the subset of GPS locations correspondingto the region). As discussed further below, FIGS. 9A-9B provide anexample of adjusting a digital map.

In addition to training and utilizing an artificial neural network, incertain embodiments, the intelligent transportation routing system 104synthesizes data to train the artificial neural network. Indeed, asmentioned above, in some embodiments, the intelligent transportationrouting system 104 can utilize millions of training route tiles togenerate a trained artificial neural network. To reduce the time andexpense associated with obtaining training data (e.g., ground-truthroute accuracy metrics and training tiles), in one or more embodiments,the intelligent transportation routing system 104 generates synthetictraining data.

In particular, as noted above, the intelligent transportation routingsystem 104 simulates route locations for a training route and transformsthe simulated route locations into simulated training GPS locations. Bysimulating the training GPS locations along a training route, theintelligent transportation routing system can generate and use syntheticroute tiles as training route tiles. FIG. 7 provides an example of theintelligent transportation routing system 104 generating training routetiles and ground-truth-route-accuracy metrics based on simulated routelocations and simulated training GPS locations.

As shown in FIG. 7, the intelligent transportation routing system 104simulates route locations 702 for a training route 704 based onstandard-traveling patterns. The training route 704 tracks simulatedlocations along a road within a road network, including subsets of routelocations 702 a, 702 b, and 702 c. The road network may be based onroads within a digital map of a real location or on roads within avirtual location generated by a computing device. Regardless of thebasis of the road network, in some embodiments, the intelligenttransportation routing system 104 generates a road network comprisingroads with various turns and slight curves ranging from 0° to 180°. Tosimulate the route locations 702 within the road network, theintelligent transportation routing system 104 generates route locationsbased on, for example, standard driving speeds, acceleration atintersections, and stops at traffic lights or stops signs.

After simulating the route locations 702 for the training route 704, theintelligent transportation routing system 104 transforms the simulatedroute locations 702 into simulated training GPS locations 708 using aGPS-noise model 706. As indicated by FIG. 7, the simulated training GPSlocations include subsets of simulated training GPS locations 708 a, 708b, and 708 c. Consistent with the disclosure above, each of the subsetsof simulated training GPS locations 708 a, 708 b, and 708 c correspondto different regions of the training route 704.

In certain embodiments, the intelligent transportation routing system104 transforms a simulated route location (g) into a simulated trainingGPS location (g) by using a GPS-noise model that simulates GPS noise asa stochastic process. As shown below, equation 1 represents one suchGPS-noise model with a mean-reverting location and velocity terms:

ĝ = g + f ${dF} = {{\begin{bmatrix}{- a} & 0 & 1 & 0 \\0 & {- a} & 0 & 1 \\0 & 0 & {- b} & 0 \\0 & 0 & 0 & {- b}\end{bmatrix}{Fdt}} + {BdW}}$In equation 1, F=[f_(x),f_(y),{dot over (f)}_(x),{dot over(f)}_(y)]^(T), B represents a diagonal matrix containing the noisedeviation for each process, and dW represents the increment in a4-dimensional Weiner process. In certain embodiments, the intelligenttransportation routing system 104 trains constants a and b in equation 1on real GPS traces to maximize similarity between simulated training GPSlocations and actual GPS locations.

As further shown in FIG. 7, after transforming the simulated routelocations 702 into the simulated training GPS locations 708, theintelligent transportation routing system 104 generates synthetic routetiles 710 a-710 c as training route tiles. In this particularembodiment, the synthetic route tiles 710 a, 710 b, and 710 crespectively include a representation of the subsets of simulatedtraining GPS locations 708 a, 708 b, and 708 c. Although not shown, incertain embodiments, the synthetic route tiles 710 a, 710 b, and 710 crespectively include a representation of both the subsets of simulatedtraining GPS locations 708 a, 708 b, and 708 c, and the subsets ofsimulated route locations 702 a, 702 b, and 702 c. In some suchembodiments, the subsets of simulated route locations 702 a, 702 b, and702 c represent equivalents of map-matched locations corresponding tothe subsets of simulated training GPS locations 708 a, 708 b, and 708 c.

In addition to generating the synthetic route tiles 710 a-710 c, theintelligent transportation routing system 104 also generates (synthetic)ground-truth-route-accuracy metrics 712 a-712 c. In particular, theintelligent transportation routing system 104 generates theground-truth-route-accuracy metrics 712 a, 712 b, and 712 c based on thesynthetic route tiles, 710 a, 710 b, and 710 c. When generatingground-truth-route-accuracy metrics for synthetic route tiles, theintelligent transportation routing system 104 determines an error valuebetween a subset of simulated training GPS locations corresponding to aregion and a subset of simulated route locations corresponding to theregion. The intelligent transportation routing system 104 then comparesthe determined error value to a noise threshold.

For example, in certain embodiments, the intelligent transportationrouting system 104 determines a ground-truth-route-accuracy metric bydetermining an error value using equation 2:

${d\left( {\hat{g}}^{1:T} \right)} = {\sum\limits_{i = 2}^{T}{d_{haversine}\left( {{\hat{g}}^{i - 1},{\hat{g}}^{i}} \right)}}$${\delta\left( g^{1:T} \right)} = \left\{ \begin{matrix}0 & {{{if}\mspace{14mu}\frac{{{d\left( {\hat{g}}^{1:T} \right)} - {d\left( g^{1:T} \right)}}}{d\left( g^{1:T} \right)}} < \alpha} \\1 & {otherwise}\end{matrix} \right.$In equation 2, ĝg^(1:T) represents a subset of simulated training GPSlocations, g^(1:T) represents a subset of simulated route locations, anda represents a GPS-noise threshold.

After determining the ground-truth-route-accuracy metrics 712 a-712 c,the intelligent transportation routing system 104 can use the syntheticroute tiles 710 a-710 c and the ground-truth-route-accuracy metrics 712a-712 c to train an artificial neural network. Accordingly, in someembodiments, the training route tiles 402 a-402 n shown in FIG. 4 may besynthetic route tiles 710 a-710 c shown in FIG. 7. Similarly, theground-truth-route-accuracy metrics 404 a-404 n shown in FIG. 4 may bethe ground-truth-route-accuracy metrics 712 a-712 c.

Turning now to FIG. 8, this figure illustrates an example of a distancedetermination that the intelligent transportation routing system 104provides to a client device. Accordingly, FIG. 8 provides one example ofupdating a route. As described above, in certain embodiments, theintelligent transportation routing system 104 determines a distancetraveled by a client device associated with a transportation vehicle. Insome such embodiments, the intelligent transportation routing system 104also provides the distance to one or more client devices forpresentation. In FIG. 8, for example, the intelligent transportationrouting system 104 (e.g., via the provider application 118) causes theprovider client device 116 to present a distance 814 of a route 808traveled by the provider client device 116.

As indicated by the route 808 in FIG. 8, the provider 120 transports therequestor 114 using the transportation vehicle 122. As thetransportation vehicle 122 travels along the route 808 from a pickuplocation to a destination location, the provider client device 116detects and transmits GPS locations to the intelligent transportationrouting system 104. Additionally, or alternatively, the requestor clientdevice 110 detects and transmits GPS locations to the intelligenttransportation routing system 104 during travel. Consistent with thedisclosure above, the intelligent transportation routing system 104determines the distance 814 of the route 808 using the methods describedabove with reference to FIG. 6 (e.g., by applying a convolutional neuralnetwork to determine a route-accuracy metric for a plurality of regions,an accurate regional distance for each region, and a total distancereflecting the sum of regional distances). The intelligenttransportation routing system 104 further sends the distance 814 to theprovider client device 116 either during travel or after the providerclient device 116 arrives at the destination location. In some suchembodiments, the intelligent transportation routing system 104repeatedly sends distance determinations to the provider client device116 during travel.

As further shown in FIG. 8, the provider client device 116 presents atransportation summary corresponding to the route 808 within anapplication graphical user interface 804 of a screen 802. Thetransportation summary includes a graphical representation of the route808, a pickup-location indicator 812, and a destination-locationindicator 810. Additionally, the transportation summary further includesa price 806 charged for transportation based on the distance 814. Asdepicted in FIG. 8, in certain embodiments, the intelligenttransportation routing system 104 causes the provider client device 116to present the distance 814 together with the price 806 and thegraphical representation of the route 808. Alternatively, theintelligent transportation routing system 104 causes the provider clientdevice 116 to present one or more of the distance 814, the price 806,and the graphical representation of the route 808.

As suggested above, in addition (or in the alternative) to providing adistance to the provider client device 116, the intelligenttransportation routing system 104 also provides the distance to therequestor client device 110 for presentation. Although not shown, theintelligent transportation routing system 104 (e.g., via the requestorapplication 112) causes the requestor client device 110 to present adistance 814 of the route 808 traveled by the requestor client device110 and/or the provider client device 116.

In addition to determining a distance to a route, in some embodiments,the intelligent transportation routing system 104 modifies digital mapsto more accurately reflect location data. FIGS. 9A-9B provide an exampleof one such modification as part of updating a route. FIG. 9A depicts adigital map 900 a before adjustment. Conversely, FIG. 9B depicts adigital map 900 b after adjustment.

As indicated by FIGS. 9A-9B, the intelligent transportation routingsystem 104 receives GPS locations from multiple client devices travelingalong a road. Using these GPS locations, the intelligent transportationrouting system 104 determines that subsets of GPS locations for a region906 are relatively clean. For example, the intelligent transportationrouting system 104 may determine route-accuracy metrics for a thresholdnumber of route tiles that indicate the subsets of GPS locations areclean (e.g., based on applying a convolutional neural network to routetiles). The intelligent transportation routing system 104 furtherdetermines that the subsets of GPS locations indicate a path thatdiffers from a corresponding map-matched road 904 a. Based on thisdetermination, the intelligent transportation routing system 104modifies the map-matched road 904 a to become a modified map-matchedroad 904 b.

As shown in FIG. 9A, the digital map 900 a includes GPS locations 902received from a client device and the map-matched road 904 a. The GPSlocations 902 include a subset of GPS locations 902 a corresponding tothe region 906. The subset of GPS locations 902 a are representative ofthe subset of GPS locations indicating a different path than themap-matched road 904 a. Based on the subset of GPS locations 902 a andother such subsets, the intelligent transportation routing system 104adjusts the map-matched road 904 a to correspond to a contour indicatedby the subset of GPS locations 902 a.

FIG. 9B depicts the digital map 900 b comprising the modifiedmap-matched road 904 b. As shown in the region 906, the modifiedmap-matched road 904 b more accurately tracks the contour of the subsetof GPS locations 902 a. While FIG. 9B illustrates an adjustment to aroundabout within the region 906, in additional embodiments, theintelligent transportation routing system 104 adjusts map-matched roadsin a variety of ways consistent with the disclosure above.

Although FIGS. 9A-9B illustrate modifying the location of roads within adigital map, the intelligent transportation routing system 104 canadjust a variety of features of a digital map based on a route-accuracymetric. For example, in one or more embodiments, the intelligenttransportation routing system 104 can also modify traffic rulesapplicable to a digital map. To illustrate, the intelligenttransportation routing system 104 can modify a one-way road to atwo-lane road based on a route-accuracy metric (e.g., based on repeateddetermination that a path traveling in the wrong direction on the on-wayroad is an accurate path). Similarly, the intelligent transportationrouting system 104 can adjust other traffic rules such as turnrestrictions (or exit lanes) based on a route-accuracy metric (e.g.,based on a repeated determination that a path traveling along arestricted turn path is an accurate path). The intelligenttransportation routing system 104 can also modify other features, suchas number of lanes or the existence of traffic lights within a digitalmap.

Turning now to FIG. 10, additional detail is provided regardingcomponents and features of the intelligent transportation routing system104. In particular, FIG. 10 shows a computing device 1002 implementingthe intelligent transportation routing system 104. In some embodiments,the computing device 1002 comprises one or more servers (e.g., theserver(s) 102) that support the intelligent transportation routingsystem 104. In other embodiments, the computing device 1002 is therequestor client device 110 or the provider client device 116. As thecomputing device 1002 suggests, in some embodiments, the server(s) 102comprise the intelligent transportation routing system 104 or portionsof the intelligent transportation routing system 104. In particular, insome instances, the server(s) 102 use the intelligent transportationrouting system 104 to perform some or all of the functions describedabove with reference to FIGS. 1-9B.

As shown in FIG. 10, the computing device 1002 include the intelligenttransportation routing system 104. The intelligent transportationrouting system 104 in turn includes, but is not limited to, a route-tilegenerator 1004, a neural network manager 1006, an application engine1008, and a storage manager 1010. The following paragraphs describe eachof these components in turn.

As shown in FIG. 10, the route-tile generator 1004 generates trainingroute tiles during training and/or route tiles for a trained artificialneural network. During training, the route-tile generator 1004identifies training GPS locations for a training route traveled by aclient device and training map-matched locations for the route.Similarly, during application, the route-tile generator 1004 identifiesGPS locations for a route traveled by a client device and map-matchedlocations for the route. Additionally, the route-tile generator 1004generates regions along a training route or route. After generating suchregions, the route-tile generator further generates training route tiles(while training) and/or route tiles (during application). In some suchimplementations, the route-tile generator 1004 synthesizes route tilesas part of the training process.

Additionally, the neural network manager 1006 trains and/or utilizes anartificial neural network. For example, in some embodiments, the neuralnetwork manager 1006 receives training route tiles and trains anartificial neural network to predict route-accuracy metrics for regionsbased on the training route tiles. Relatedly, the neural network manager1006 can also generate ground-truth-route-accuracy metrics.Additionally, the neural network manager 1006 also generates a trainedartificial neural network. After training the artificial neural network,in some embodiments, the neural network manager 1006 also receives routetiles and utilizes the artificial neural network to determineroute-accuracy metrics for regions based on route tiles.

As further shown in FIG. 10, the application engine 1008 utilizesroute-accuracy metrics to perform various acts. For example, in someembodiments, the application engine 1008 determines a distance of aroute based on route-accuracy metrics. Additionally, or alternatively,in some embodiments, the application engine 1008 determines a locationof a client device, such as by determining a location of the clientdevice within a particular region. In certain implementations, theapplication engine 1008 adjusts a digital map based on GPS locations.

As noted above, the intelligent transportation routing system 104further includes the storage manager 1010. Among other things, thestorage manager 1010 maintains an artificial neural network 1012, GPSlocations 1014, map-matched locations 1016, route tiles 1018, and/orroute-accuracy metrics 1020. In some embodiments, the artificial neuralnetwork 1012 comprises a machine learning model that can be trained. Thestorage manager 1010 maintains the artificial neural network 1012 bothduring and/or after the neural network manager 1006 generates a trainedartificial neural network. Additionally, in some embodiments, the GPSlocations 1014 comprise data files from which the route-tile generator1004 identifies GPS locations. Relatedly, in certain embodiments, themap-matched locations 1016 comprise data files from which the route-tilegenerator 1004 identifies map-matched locations. Alternatively, themap-matched locations 1016 comprise map-matched locations generated bythe route-tile generator 1004. In some embodiments, the route tiles 1018include the training route tiles and/or the route tiles generated by theroute-tile generator 1004. Finally, in certain implementations, theroute-accuracy metrics 1020 comprise data files that include theground-truth-route-accuracy metrics and/or the route-accuracy metricsgenerated by the neural network manager 1006.

Turning now to FIG. 11, this figure illustrates a flowchart of a seriesof acts 1100 of training an artificial neural network to use trainingroute tiles to predict route-accuracy metrics for regions along atraining route traveled by a client device in accordance with one ormore embodiments. While FIG. 11 illustrates acts according to oneembodiment, alternative embodiments may omit, add to, reorder, and/ormodify any of the acts shown in FIG. 11. The acts of FIG. 11 can beperformed as part of a method. Alternatively, a non-transitory computerreadable storage medium can comprise instructions that, when executed byone or more processors, cause a computing device to perform the actsdepicted in FIG. 11. In still further embodiments, a system can performthe acts of FIG. 11.

As shown in FIG. 11, the acts 1100 include an act 1110 of identifyingtraining GPS locations and training map-matched locations for a trainingroute. For example, in some embodiments, the act 1110 includesidentifying training GPS locations for a training route traveled by aclient device and training map-matched locations for the training route,wherein the client device is associated with a transportation vehicle.

In some implementations, identifying the training GPS locationscomprises receiving GPS locations detected by the client device alongthe training route. By contrast, in certain embodiments, the trainingGPS locations for the training route comprise simulated training GPSlocations, and generating the simulated training GPS locationscomprises: creating simulated route locations for the training routewithin a road network based on standard-traveling patterns oftransportation vehicles; and generating the simulated training GPSlocations by transforming the simulated route locations based on aGPS-noise model. Relatedly, in certain embodiments, generating theground-truth-route-accuracy metric for the region comprises determiningan error value between a subset of the simulated training GPS locationscorresponding to the region and a subset of the simulated routelocations corresponding to the region; and comparing the determinederror value to a noise threshold.

As further shown in FIG. 11, the acts 1100 include an act 1120 ofgenerating regions along the training route. For example, in someembodiments, the act 1120 includes generating a plurality of regionsalong the training route, wherein a region comprises a subset oftraining GPS locations and a subset of training map-matched locations.

As further shown in FIG. 11, the acts 1100 include an act 1130 ofgenerating a training route tile for a region. For example, in someembodiments, the act 1130 includes generating a training route tile forthe region based on the subset of training GPS locations and the subsetof training map-matched locations.

As suggested above, in certain implementations, generating the trainingroute tile for the region comprises generating a first image matrixcomprising a representation of the subset of training GPS locations; andgenerating a second image matrix comprising a representation of thesubset of training map-matched locations.

As further shown in FIG. 11, the acts 1100 include an act 1140 ofutilizing an artificial neural network to predict a route-accuracymetric for the region. For instance, in some embodiments, the act 1140includes utilizing an artificial neural network to predict aroute-accuracy metric for the region based on the training route tile.

As further shown in FIG. 11, the acts 1100 include an act 1150 ofgenerating a trained artificial neural network by comprising theroute-accuracy metric to a ground-truth-route-accuracy metric. Forexample, in some embodiments, the act 1150 includes generating a trainedartificial neural network by comparing the route-accuracy metric to aground-truth-route-accuracy metric for the region.

In some such embodiments, the ground-truth-route-accuracy metric for theregion comprises an accuracy classifier for the training route tileindicating a level of noise of the subset of training GPS locations; andthe route-accuracy metric for the region comprises a predicted accuracyclassifier for the training route tile indicating a predicted level ofnoise of the subset of training GPS locations.

In addition to the acts 1110-1150, in certain embodiments, the acts 1100further include, generating the training map-matched locations bymodifying the training GPS locations to correspond to roads within adigital map.

When training the artificial neural network, in one or more embodiments,the acts 1100 further include determining a first estimated path of theclient device within the region based on the subset of training GPSlocations and a second estimated path of the client device with theregion based on the subset of training map-matched locations, wherein:the first image matrix comprises a first set of pixels that representthe first estimated path of the client device within the region and asecond set of pixels that represent positions outside of the firstestimated path within the region; and the second image matrix comprisesa third set of pixels that represent the second estimated path of theclient device within the region and a fourth set of pixels thatrepresent positions outside of the second estimated path within theregion.

As noted above, in some embodiments, the intelligent transportationrouting system 104 analyzes multiple training route tiles. For example,in one or more embodiments, the acts 1100 further include utilizing theartificial neural network to predict an additional route-accuracy metricfor an additional region based on an additional training route tile; andgenerating the trained artificial neural network by comparing theadditional route-accuracy metric to an additionalground-truth-route-accuracy metric.

Turning now to FIG. 12, this figure illustrates a flowchart of a seriesof acts 1200 of using an artificial neural network to determineroute-accuracy metrics for regions along a route traveled by a clientdevice in accordance with one or more embodiments. While FIG. 12illustrates acts according to one embodiment, alternative embodimentsmay omit, add to, reorder, and/or modify any of the acts shown in FIG.12. The acts of FIG. 12 can be performed as part of a method.Alternatively, a non-transitory computer readable storage medium cancomprise instructions that, when executed by one or more processors,cause a computing device to perform the acts depicted in FIG. 12. Instill further embodiments, a system can perform the acts of FIG. 12.

As shown in FIG. 12, the acts 1200 include an act 1210 of identifying aset of GPS locations and a corresponding set of map-matched locationsfor a route. For example, in some embodiments, the act 1210 includesidentifying a set of GPS locations and a corresponding set ofmap-matched locations for a route traveled by a client device. Incertain implementations, the client device is associated with atransportation vehicle.

As further shown in FIG. 12, the acts 1200 include an act 1220 ofsegmenting the set of GPS locations and the corresponding set ofmap-matched locations into regions along the route. For instance, incertain embodiments, the act 1220 includes segmenting the set of GPSlocations and the corresponding set of map-matched locations into one ormore regions along the route, wherein each region of the one or moreregions comprises a subset of GPS locations and a subset of map-matchedlocations.

As further shown in FIG. 12, the acts 1200 include an act 1230 ofutilizing a trained artificial neural network to determine aroute-accuracy metric for each region. As noted above, theroute-accuracy metric may take a variety of forms. For example, in someembodiments, the route-accuracy metric comprises a classifier indicatingthe subset of GPS locations; and determining the distance of the routebased on the route-accuracy metric comprises determining a regionaldistance traveled by the client device within the region based on thesubset of GPS locations. In some such embodiments, the acts 1200 furtherinclude, based on the classifier indicating the subset of GPS locations,adjusting a digital map for the region to correspond to the subset ofGPS locations.

As further shown in FIG. 12, the acts 1200 include an act 1240 ofselecting, for each region, one of the subset of GPS locations or thesubset of map-matched locations. For instance, in some embodiments, theact 1240 includes selecting, for each region, one of the subset of GPSlocations or the subset of map-matched locations based on theroute-accuracy metric.

Finally, as shown in FIG. 12, the acts 1200 include an act 1250 ofupdating the route to include the selected subset of GPS locations orthe selected subset of map-matched locations for each region. Forexample, in some embodiments, updating the route comprises determining adistance of the route based on the selected subset of GPS locations orthe selected subset of map-matched locations for each region. As anotherexample, in certain implementations, updating the route comprisesadjusting a digital map for the region to correspond to the selectedsubset of GPS locations or the selected subset of map-matched locationsfor each region.

In addition to the acts 1210-1250, in certain embodiments, the acts 1200further include generating, for each region, a route tile for the regionbased on the subset of GPS locations for the region and the subset ofmap-matched locations for the region. In some such embodiments,utilizing the trained artificial neural network to determine theroute-accuracy metric for each region comprises utilizing the trainedartificial neural network to analyze the route tile for the region togenerate the route-accuracy metric for the region.

Relatedly, in certain implementations, generating the route tile foreach region comprises generating a first image matrix for the regioncomprising a representation of the subset of GPS locations for theregion; and generating a second image matrix for the region comprising arepresentation of the subset of map-matched locations for the region. Insome such implementations, generating the route tile for each regioncomprises determining a first estimated path of the client device withinthe region based on the subset of GPS locations for the region and asecond estimated path of the client device with the region based on thesubset of map-matched locations for the region; the first image matrixcomprises a first set of pixels that represent the first estimated pathof the client device within the region and a second set of pixels thatrepresent positions outside of the first estimated path within theregion; and the second image matrix comprises a third set of pixels thatrepresent the second estimated path of the client device within theregion and a fourth set of pixels that represent positions outside ofthe second estimated path within the region.

As noted above, the intelligent transportation routing system 104 trainsan artificial network. In some such embodiments, generating the trainedartificial neural network comprises generating a training route tile fora training region along a training route based on training GPS locationsand corresponding training map-matched locations; utilizing anartificial neural network to predict a route-accuracy metric for thetraining region based on the training route tile; and generating thetrained artificial neural network by comparing the route-accuracy metricto a ground-truth-route-accuracy metric for the training region.

Relatedly, in some implementations, generating the simulated trainingGPS locations comprises creating simulated route locations for thetraining route within a road network based on standard-travelingpatterns of transportation vehicles; and generating the simulatedtraining GPS locations by transforming the simulated route locationsbased on a GPS-noise model. Additionally, in one or more embodiments,generate the ground-truth-route-accuracy metric for the training regioncomprises determining an error value between the simulated training GPSlocations and the simulated route locations; and comparing thedetermined error value to a noise threshold.

In some embodiments, the acts 1200 include an alternative set of acts.For example, in some embodiments, the acts 1200 include identifying GPSlocations and map-matched locations for a route. For example, in someembodiments, the acts 1200 include identifying GPS locations for a routetraveled by a client device and map-matched locations for the route,wherein the client device is associated with a transportation vehicle.

Additionally, in certain embodiments, the acts 1200 include generatingregions along the route. For instance, in certain embodiments, the acts1200 include generating a plurality of regions along the route, whereina region comprises a subset of GPS locations and a subset of map-matchedlocations.

Moreover, in some implementations, the acts 1200 include generating aroute tile for the region. For example, in some implementations, the act1200 includes generating a route tile for the region based on the subsetof GPS locations and the subset of map-matched locations.

Further, in certain implementations, the acts 1200 include utilizing anartificial neural network to determine a route-accuracy metric for theregion. For instance, in some embodiments, the acts 1200 includesutilizing an artificial neural network to determine a route-accuracymetric for the region based on the route tile. In certain embodiments,the route-accuracy metric for the region comprises an accuracyclassifier for the route tile indicating a level of noise of the subsetof GPS locations.

Finally, in some such embodiments, the acts 1200 include determining adistance of the route based on the route-accuracy metric. As notedabove, the route-accuracy metric may take a variety of forms. Forexample, in some embodiments, the route-accuracy metric comprises aclassifier indicating the subset of GPS locations; and determining thedistance of the route based on the route-accuracy metric comprisesdetermining a regional distance traveled by the client device within theregion based on the subset of GPS locations. In some such embodiments,the acts 1200 further include, based on the classifier indicating thesubset of GPS locations, adjusting a digital map for the region tocorrespond to the subset of GPS locations.

Alternatively, in some embodiments, the route-accuracy metric comprisesa classifier indicating the subset of map-matched locations; anddetermining the distance of the route based on the route-accuracy metriccomprises determining a regional distance traveled by the client devicewithin the region based on the subset of map-matched locations.

Additionally, in certain embodiments, the route-accuracy metric for theregion comprises a regional distance traveled by the client devicewithin the region based on the route tile; and determining the distanceof the route based on the route-accuracy metric comprises determiningthe distance of the route based on the regional distance traveled by theclient device within the region.

As suggested above, in some embodiments, the acts 1200 further includeutilizing the artificial neural network to determine an additionalroute-accuracy metric for an additional region based on an additional aroute tile; and determining the distance of the route based on theadditional route-accuracy metric for the additional region and theroute-accuracy metric for the region.

Embodiments of the present disclosure may comprise or utilize a specialpurpose or general-purpose computer including computer hardware, suchas, for example, one or more processors and system memory, as discussedin greater detail below. Embodiments within the scope of the presentdisclosure also include physical and other computer-readable media forcarrying or storing computer-executable instructions and/or datastructures. In particular, one or more of the processes described hereinmay be implemented at least in part as instructions embodied in anon-transitory computer-readable medium and executable by one or morecomputing devices (e.g., any of the media content access devicesdescribed herein). In general, a processor (e.g., a microprocessor)receives instructions, from a non-transitory computer-readable medium,(e.g., a memory, etc.), and executes those instructions, therebyperforming one or more processes, including one or more of the processesdescribed herein.

Computer-readable media can be any available media that can be accessedby a general purpose or special purpose computer system, including byone or more servers. Computer-readable media that storecomputer-executable instructions are non-transitory computer-readablestorage media (devices). Computer-readable media that carrycomputer-executable instructions are transmission media. Thus, by way ofexample, and not limitation, embodiments of the disclosure can compriseat least two distinctly different kinds of computer-readable media:non-transitory computer-readable storage media (devices) andtransmission media.

Non-transitory computer-readable storage media (devices) includes RAM,ROM, EEPROM, CD-ROM, solid state drives (“SSDs”) (e.g., based on RAM),Flash memory, phase-change memory (“PCM”), other types of memory, otheroptical disk storage, magnetic disk storage or other magnetic storagedevices, or any other medium which can be used to store desired programcode means in the form of computer-executable instructions or datastructures and which can be accessed by a general purpose or specialpurpose computer.

Further, upon reaching various computer system components, program codemeans in the form of computer-executable instructions or data structurescan be transferred automatically from transmission media tonon-transitory computer-readable storage media (devices) (or viceversa). For example, computer-executable instructions or data structuresreceived over a network or data link can be buffered in RAM within anetwork interface module (e.g., a “NIC”), and then eventuallytransferred to computer system RAM and/or to less volatile computerstorage media (devices) at a computer system. Thus, it should beunderstood that non-transitory computer-readable storage media (devices)can be included in computer system components that also (or evenprimarily) utilize transmission media.

Computer-executable instructions comprise, for example, instructions anddata which, when executed at a processor, cause a general-purposecomputer, special purpose computer, or special purpose processing deviceto perform a certain function or group of functions. In someembodiments, computer-executable instructions are executed on ageneral-purpose computer to turn the general-purpose computer into aspecial purpose computer implementing elements of the disclosure. Thecomputer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, or evensource code. Although the subject matter has been described in languagespecific to structural features and/or methodological acts, it is to beunderstood that the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described above.Rather, the described features and acts are disclosed as example formsof implementing the claims.

Those skilled in the art will appreciate that the disclosure may bepracticed in network computing environments with many types of computersystem configurations, including, virtual reality devices, personalcomputers, desktop computers, laptop computers, message processors,hand-held devices, multi-processor systems, microprocessor-based orprogrammable consumer electronics, network PCs, minicomputers, mainframecomputers, mobile telephones, PDAs, tablets, pagers, routers, switches,and the like. The disclosure may also be practiced in distributed systemenvironments where local and remote computer systems, which are linked(either by hardwired data links, wireless data links, or by acombination of hardwired and wireless data links) through a network,both perform tasks. In a distributed system environment, program modulesmay be located in both local and remote memory storage devices.

Embodiments of the present disclosure can also be implemented in cloudcomputing environments. In this description, “cloud computing” isdefined as a model for enabling on-demand network access to a sharedpool of configurable computing resources. For example, cloud computingcan be employed in the marketplace to offer ubiquitous and convenienton-demand access to the shared pool of configurable computing resources.The shared pool of configurable computing resources can be rapidlyprovisioned via virtualization and released with low management effortor service provider interaction, and then scaled accordingly.

A cloud-computing model can be composed of various characteristics suchas, for example, on-demand self-service, broad network access, resourcepooling, rapid elasticity, measured service, and so forth. Acloud-computing model can also expose various service models, such as,for example, Software as a Service (“SaaS”), Platform as a Service(“PaaS”), and Infrastructure as a Service (“IaaS”). A cloud-computingmodel can also be deployed using different deployment models such asprivate cloud, community cloud, public cloud, hybrid cloud, and soforth. In this description and in the claims, a “cloud-computingenvironment” is an environment in which cloud computing is employed.

FIG. 13 illustrates, in block diagram form, an exemplary computingdevice 1300 that may be configured to perform one or more of theprocesses described above. One will appreciate that the intelligenttransportation routing system 104 can comprise implementations of thecomputing device 1300, including, but not limited to, the server(s) 102,the requestor client devices 110, and the provider client device 116. Asshown by FIG. 13, the computing device can comprise a processor 1302,memory 1304, a storage device 1306, an I/O interface 1308, and acommunication interface 1310. In certain embodiments, the computingdevice 1300 can include fewer or more components than those shown inFIG. 13. Components of computing device 1300 shown in FIG. 13 will nowbe described in additional detail.

In particular embodiments, processor(s) 1302 includes hardware forexecuting instructions, such as those making up a computer program. Asan example, and not by way of limitation, to execute instructions,processor(s) 1302 may retrieve (or fetch) the instructions from aninternal register, an internal cache, memory 1304, or a storage device1306 and decode and execute them.

The computing device 1300 includes memory 1304, which is coupled to theprocessor(s) 1302. The memory 1304 may be used for storing data,metadata, and programs for execution by the processor(s) 1302. Thememory 1304 may include one or more of volatile and non-volatilememories, such as Random Access Memory (“RAM”), Read Only Memory(“ROM”), a solid-state disk (“SSD”), Flash, Phase Change Memory (“PCM”),or other types of data storage. The memory 1304 may be internal ordistributed memory.

The computing device 1300 includes a storage device 1306 for storingdata or instructions. As an example, and not by way of limitation,storage device 1306 can comprise a non-transitory storage mediumdescribed above. The storage device 1306 may include a hard disk drive(“HDD”), flash memory, a Universal Serial Bus (“USB”) drive or acombination of these or other storage devices.

The computing device 1300 also includes one or more input or output(“I/O”) interface 1308, which are provided to allow a user (e.g.,requestor or provider) to provide input to (such as user strokes),receive output from, and otherwise transfer data to and from thecomputing device 1300. These I/O interface 1308 may include a mouse,keypad or a keyboard, a touch screen, camera, optical scanner, networkinterface, modem, other known I/O devices or a combination of such I/Ointerface 1308. The touch screen may be activated with a stylus or afinger.

The I/O interface 1308 may include one or more devices for presentingoutput to a user, including, but not limited to, a graphics engine, adisplay (e.g., a display screen), one or more output providers (e.g.,display providers), one or more audio speakers, and one or more audioproviders. In certain embodiments, the I/O interface 1308 is configuredto provide graphical data to a display for presentation to a user. Thegraphical data may be representative of one or more graphical userinterfaces and/or any other graphical content as may serve a particularimplementation.

The computing device 1300 can further include a communication interface1310. The communication interface 1310 can include hardware, software,or both. The communication interface 1310 can provide one or moreinterfaces for communication (such as, for example, packet-basedcommunication) between the computing device and one or more othercomputing devices 1300 or one or more networks. As an example, and notby way of limitation, communication interface 1310 may include a networkinterface controller (“NIC”) or network adapter for communicating withan Ethernet or other wire-based network or a wireless NIC (“WNIC”) orwireless adapter for communicating with a wireless network, such as aWI-FI. The computing device 1300 can further include a bus 1312. The bus1312 can comprise hardware, software, or both that couples components ofcomputing device 1300 to each other.

FIG. 14 illustrates an example network environment 1400 of anintelligent transportation routing system. The network environment 1400includes an intelligent transportation routing system 1402, a clientdevice 1406, and a vehicle subsystem 1408 connected to each other by anetwork 1404. Although FIG. 14 illustrates a particular arrangement ofthe intelligent transportation routing system 1402, client device 1406,vehicle subsystem 1408, and network 1404, this disclosure contemplatesany suitable arrangement of the intelligent transportation routingsystem 1402, client device 1406, vehicle subsystem 1408, and network1404. As an example, and not by way of limitation, two or more of theclient device 1406, intelligent transportation routing system 1402, andvehicle subsystem 1408 communicate directly, bypassing network 1404. Asanother example, two or more of the client device 1406, intelligenttransportation routing system 1402, and vehicle subsystem 1408 may bephysically or logically co-located with each other in whole or in part.Moreover, although FIG. 14 illustrates a particular number of clientdevices 1406, intelligent transportation routing systems 1402, vehiclesubsystems 1408, and networks 1404, this disclosure contemplates anysuitable number of client devices 1406, intelligent transportationrouting systems 1402, vehicle subsystems 1408, and networks 1404. As anexample, and not by way of limitation, network environment 1400 mayinclude multiple client devices 1406, intelligent transportation routingsystems 1402, vehicle subsystems 1408, and networks 1404.

This disclosure contemplates any suitable network 1404. As an example,and not by way of limitation, one or more portions of network 1404 mayinclude an ad hoc network, an intranet, an extranet, a virtual privatenetwork (“VPN”), a local area network (“LAN”), a wireless LAN (“WLAN”),a wide area network (“WAN”), a wireless WAN (“WWAN”), a metropolitanarea network (“MAN”), a portion of the Internet, a portion of the PublicSwitched Telephone Network (“PSTN”), a cellular telephone network, or acombination of two or more of these. Network 1104 may include one ormore networks 1404.

Links may connect client device 1406, intelligent transportation routingsystem 1402, and vehicle subsystem 1408 to network 1404 or to eachother. This disclosure contemplates any suitable links. In particularembodiments, one or more links include one or more wireline (such as forexample Digital Subscriber Line (“DSL”) or Data Over Cable ServiceInterface Specification (“DOCSIS”), wireless (such as for example Wi-Fior Worldwide Interoperability for Microwave Access (“WiMAX”), or optical(such as for example Synchronous Optical Network (“SONET”) orSynchronous Digital Hierarchy (“SDH”) links. In particular embodiments,one or more links each include an ad hoc network, an intranet, anextranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, a portion of theInternet, a portion of the PSTN, a cellular technology-based network, asatellite communications technology-based network, another link, or acombination of two or more such links. Links need not necessarily be thesame throughout network environment 1400. One or more first links maydiffer in one or more respects from one or more second links.

In particular embodiments, client device 1406 may be an electronicdevice including hardware, software, or embedded logic components or acombination of two or more such components and capable of carrying outthe appropriate functionalities implemented or supported by clientdevice 1406. As an example, and not by way of limitation, a clientdevice 1406 may include any of the computing devices discussed above inrelation to FIG. 13. A client device 1406 may enable a network user atclient device 1406 to access network 1404. A client device 1406 mayenable its user to communicate with other users at other client devices1406.

In particular embodiments, client device 1406 may include a requestorapplication or a web browser, such as MICROSOFT INTERNET EXPLORER,GOOGLE CHROME or MOZILLA FIREFOX, and may have one or more add-ons,plug-ins, or other extensions, such as TOOLBAR or YAHOO TOOLBAR. A userat client device 1406 may enter a Uniform Resource Locator (“URL”) orother address directing the web browser to a particular server (such asserver), and the web browser may generate a Hyper Text Transfer Protocol(“HTTP”) request and communicate the HTTP request to server. The servermay accept the HTTP request and communicate to client device 1406 one ormore Hyper Text Markup Language (“HTML”) files responsive to the HTTPrequest. Client device 1406 may render a webpage based on the HTML filesfrom the server for presentation to the user. This disclosurecontemplates any suitable webpage files. As an example, and not by wayof limitation, webpages may render from HTML files, Extensible HyperText Markup Language (“XHTML”) files, or Extensible Markup Language(“XML”) files, according to particular needs. Such pages may alsoexecute scripts such as, for example and without limitation, thosewritten in JAVASCRIPT, JAVA, MICROSOFT SILVERLIGHT, combinations ofmarkup language and scripts such as AJAX (Asynchronous JAVASCRIPT andXML), and the like. Herein, reference to a webpage encompasses one ormore corresponding webpage files (which a browser may use to render thewebpage) and vice versa, where appropriate.

In particular embodiments, intelligent transportation routing system1402 may be a network-addressable computing system that can host atransportation matching network. Intelligent transportation routingsystem 1402 may generate, store, receive, and send data, such as, forexample, user-profile data, concept-profile data, text data,transportation request data, GPS location data, provider data, requestordata, vehicle data, or other suitable data related to the transportationmatching network. This may include authenticating the identity ofproviders and/or vehicles who are authorized to provide transportationservices through the intelligent transportation routing system 1402. Inaddition, the dynamic transportation matching system may manageidentities of service requestors such as users/requestors. Inparticular, the dynamic transportation matching system may maintainrequestor data such as driving/riding histories, personal data, or otheruser data in addition to navigation and/or traffic management servicesor other location services (e.g., GPS services).

In particular embodiments, the intelligent transportation routing system1402 may manage transportation matching services to connect auser/requestor with a vehicle and/or provider. By managing thetransportation matching services, the intelligent transportation routingsystem 1402 can manage the distribution and allocation of resources fromthe vehicle subsystems 108 a and 108 n and user resources such as GPSlocation and availability indicators, as described herein.

Intelligent transportation routing system 1402 may be accessed by theother components of network environment 1400 either directly or vianetwork 1404. In particular embodiments, intelligent transportationrouting system 1402 may include one or more servers. Each server may bea unitary server or a distributed server spanning multiple computers ormultiple datacenters. Servers may be of various types, such as, forexample and without limitation, web server, news server, mail server,message server, advertising server, file server, application server,exchange server, database server, proxy server, another server suitablefor performing functions or processes described herein, or anycombination thereof. In particular embodiments, each server may includehardware, software, or embedded logic components or a combination of twoor more such components for carrying out the appropriate functionalitiesimplemented or supported by server. In particular embodiments,intelligent transportation routing system 1402 may include one or moredata stores. Data stores may be used to store various types ofinformation. In particular embodiments, the information stored in datastores may be organized according to specific data structures. Inparticular embodiments, each data store may be a relational, columnar,correlation, or other suitable database. Although this disclosuredescribes or illustrates particular types of databases, this disclosurecontemplates any suitable types of databases. Particular embodiments mayprovide interfaces that enable a client device 1406, or an intelligenttransportation routing system 1402 to manage, retrieve, modify, add, ordelete, the information stored in data store.

In particular embodiments, intelligent transportation routing system1402 may provide users with the ability to take actions on various typesof items or objects, supported by intelligent transportation routingsystem 1402. As an example, and not by way of limitation, the items andobjects may include transportation matching networks to which users ofintelligent transportation routing system 1402 may belong, vehicles thatusers may request, location designators, computer-based applicationsthat a user may use, transactions that allow users to buy or sell itemsvia the service, interactions with advertisements that a user mayperform, or other suitable items or objects. A user may interact withanything that is capable of being represented in intelligenttransportation routing system 1402 or by an external system of athird-party system, which is separate from intelligent transportationrouting system 1402 and coupled to intelligent transportation routingsystem 1402 via a network 1404.

In particular embodiments, intelligent transportation routing system1402 may be capable of linking a variety of entities. As an example, andnot by way of limitation, intelligent transportation routing system 1402may enable users to interact with each other or other entities, or toallow users to interact with these entities through an applicationprogramming interfaces (“API”) or other communication channels.

In particular embodiments, intelligent transportation routing system1402 may include a variety of servers, sub-systems, programs, modules,logs, and data stores. In particular embodiments, intelligenttransportation routing system 1402 may include one or more of thefollowing: a web server, action logger, API-request server,relevance-and-ranking engine, content-object classifier, notificationcontroller, action log, third-party-content-object-exposure log,inference module, authorization/privacy server, search module,advertisement-targeting module, user-interface module, user-profilestore, connection store, third-party content store, or location store.Intelligent transportation routing system 1402 may also include suitablecomponents such as network interfaces, security mechanisms, loadbalancers, failover servers, management-and-network-operations consoles,other suitable components, or any suitable combination thereof. Inparticular embodiments, intelligent transportation routing system 1402may include one or more user-profile stores for storing user profiles. Auser profile may include, for example, biographic information,demographic information, behavioral information, social information, orother types of descriptive information, such as work experience,educational history, hobbies or preferences, interests, affinities, orlocation.

The web server may include a mail server or other messagingfunctionality for receiving and routing messages between intelligenttransportation routing system 1402 and one or more client devices 1406.An action logger may be used to receive communications from a web serverabout a user's actions on or off intelligent transportation routingsystem 1402. In conjunction with the action log, athird-party-content-object log may be maintained of user exposures tothird-party-content objects. A notification controller may provideinformation regarding content objects to a client device 1406.Information may be pushed to a client device 1406 as notifications, orinformation may be pulled from client device 1406 responsive to arequest received from client device 1406. Authorization servers may beused to enforce one or more privacy settings of the users of intelligenttransportation routing system 1402. A privacy setting of a userdetermines how particular information associated with a user can beshared. The authorization server may allow users to opt in to or opt outof having their actions logged by intelligent transportation routingsystem 1402 or shared with other systems, such as, for example, bysetting appropriate privacy settings. Third-party-content-object storesmay be used to store content objects received from third parties.Location stores may be used for storing location information receivedfrom client devices 1406 associated with users.

In addition, the vehicle subsystem 1408 can include a human-operatedvehicle or an autonomous vehicle. A provider of a human-operated vehiclecan perform maneuvers to pick up, transport, and drop off one or morerequestors according to the embodiments described herein. In certainembodiments, the vehicle subsystem 1408 can include an autonomousvehicle—i.e., a vehicle that does not require a human operator. When atransportation vehicle is an autonomous vehicle, the transportationvehicle may include additional components not depicted in FIG. 1 or FIG.14, such as location components, one or more sensors by which theautonomous vehicle navigates, and/or other components necessary tonavigate without a provider (or with minimal interactions with aprovider). In these embodiments, the vehicle subsystem 1408 can performmaneuvers, communicate, and otherwise function without the aid of ahuman provider, in accordance with available technology.

Additionally, in some embodiments, the vehicle subsystem 1408 includes ahybrid self-driving vehicle with both self-driving functionality andsome human operator interaction. This human operator interaction maywork in concert with or independent of the self-driving functionality.In other embodiments, the vehicle subsystems 1408 includes an autonomousprovider that acts as part of the transportation vehicle, such as acomputer-based navigation and driving system that acts as part of atransportation vehicle. Regardless of whether a transportation vehicleis associated with a provider, a transportation vehicle optionallyincludes a locator device, such as a GPS device, that determines thelocation of the transportation vehicle within the vehicle subsystem1408.

In particular embodiments, the vehicle subsystem 1408 may include one ormore sensors incorporated therein or associated thereto. For example,sensor(s) 1410 can be mounted on the top of the vehicle subsystem 1408or else can be located within the interior of the vehicle subsystem1408. In certain embodiments, the sensor(s) 1410 can be located inmultiple areas at once—i.e., split up throughout the vehicle subsystem1408 so that different components of the sensor(s) 1410 can be placed indifferent locations in accordance with optimal operation of thesensor(s) 1410. In these embodiments, the sensor(s) 1410 can include aLIDAR sensor and an inertial measurement unit (“IMU”) including one ormore accelerometers, one or more gyroscopes, and one or moremagnetometers. The sensor(s) 1410 can additionally or alternativelyinclude a wireless IMU (“WIMU”), one or more cameras, one or moremicrophones, or other sensors or data input devices capable of receivingand/or recording information relating to navigating a route to pick up,transport, and/or drop off a requestor.

In particular embodiments, the vehicle subsystem 1408 may include acommunication device capable of communicating with the client device1406 and/or the intelligent transportation routing system 1402. Forexample, the vehicle subsystem 1408 can include an on-board computingdevice communicatively linked to the network 1404 to transmit andreceive data such as GPS location information, sensor-relatedinformation, requestor location information, or other relevantinformation.

In the foregoing specification, the invention has been described withreference to specific exemplary embodiments thereof. Various embodimentsand aspects of the invention(s) are described with reference to detailsdiscussed herein, and the accompanying drawings illustrate the variousembodiments. The description above and drawings are illustrative of theinvention and are not to be construed as limiting the invention.Numerous specific details are described to provide a thoroughunderstanding of various embodiments of the present invention.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. For example, the methods described herein may beperformed with less or more steps/acts or the steps/acts may beperformed in differing orders. Additionally, the steps/acts describedherein may be repeated or performed in parallel with one another or inparallel with different instances of the same or similar steps/acts. Thescope of the invention is, therefore, indicated by the appended claimsrather than by the foregoing description. All changes that come withinthe meaning and range of equivalency of the claims are to be embracedwithin their scope.

We claim:
 1. A system comprising: at least one processor; and at leastone non-transitory computer readable storage medium storing instructionsthat, when executed by the at least one processor, cause the system to:identify a set of training GPS locations and a set of trainingmap-matched locations for a training route; analyze the set of trainingGPS locations and the set of training map-matched locations utilizing anartificial neural network to generate predicted-route-accuracy metrics;train the artificial neural network to select between GPS locations andmap-matched locations by comparing the predicted-route-accuracy metricsto ground-truth-route-accuracy metrics; and modify a digital map byutilizing the trained artificial neural network to analyze GPS locationsfor a route travelled by a client device and map-matched locations ofthe digital map.
 2. The system of claim 1, further comprisinginstructions that, when executed by the at least one processor, causethe system to: determine a set of training regions corresponding to thetraining route; and generate a training route tile for a training regionof the set of training regions based on a subset of training GPSlocations and a subset of training map-matched locations correspondingto the training region.
 3. The system of claim 2, further comprisinginstructions that, when executed by the at least one processor, causethe system to generate the training route tile by: generating a firsttraining image for the training region based on the subset of trainingGPS locations; and generating a second training image for the trainingregion based on the subset of training map-matched locations.
 4. Thesystem of claim 2, further comprising instructions that, when executedby the at least one processor, cause the system to analyze the trainingroute tile utilizing the artificial neural network to generate apredicted-route-accuracy-metric, the predicted-route-accuracy metriccomprising at least one of: a predicted accuracy classifier, a predicteddistance, a predicted path, or a predicted GPS noise level.
 5. Thesystem of claim 4, further comprising instructions that, when executedby the at least one processor, cause the system to: identify aground-truth-route-accuracy metric, the ground-truth-route-accuracymetric comprising at least one of a ground-truth accuracy classifier, aground-truth distance, a ground-truth path, or a ground-truth GPS noiselevel; and compare the predicted-route-accuracy metrics to theground-truth-route-accuracy metrics by applying a loss function to thepredicted-route-accuracy metric and the ground-truth-route-accuracymetric.
 6. The system of claim 1, wherein the set of training GPSlocations comprise simulated training GPS locations, the system furthercomprising instructions that, when executed by the at least oneprocessor, cause the system to generate the simulated training GPSlocations by: determining a road network comprising the training route;creating simulated route locations for the training route within theroad network; and generating the simulated training GPS locations bytransforming the simulated route locations based on a GPS-noise model.7. The system of claim 6, further comprising instructions that, whenexecuted by the at least one processor, cause the system to generate thesimulated training GPS locations based on the GPS-noise model byutilizing a diagonal matrix comprising a noise deviation and Weinerprocess.
 8. The system of claim 6, further comprising instructions that,when executed by the at least one processor, cause the system to trainthe GPS-noise model based on historical GPS traces.
 9. A non-transitorycomputer readable medium storing instructions thereon that, whenexecuted by at least one processor, cause a computer system to: identifya set of training GPS locations and a set of training map-matchedlocations for a training route; analyze the set of training GPSlocations and the set of training map-matched locations utilizing anartificial neural network to generate predicted-route-accuracy metrics;train the artificial neural network to select between GPS locations andmap-matched locations by comparing the predicted-route-accuracy metricsto ground-truth-route-accuracy metrics; and modify a digital map byutilizing the trained artificial neural network to analyze GPS locationsfor a route travelled by a client device and map-matched locations ofthe digital map.
 10. The non-transitory computer readable medium ofclaim 9, further comprising instructions that, when executed by the atleast one processor, cause the computer system to: determine a set oftraining regions corresponding to the training route; and generate atraining route tile for a training region of the set of training regionsbased on a subset of training GPS locations and a subset of trainingmap-matched locations corresponding to the training region.
 11. Thenon-transitory computer readable medium of claim 10, further comprisinginstructions that, when executed by the at least one processor, causethe computer system to generate the training route tile by: generating afirst training image for the training region based on the subset oftraining GPS locations; and generating a second training image for thetraining region based on the subset of training map-matched locations.12. The non-transitory computer readable medium of claim 10, furthercomprising instructions that, when executed by the at least oneprocessor, cause the computer system to analyze the training route tileutilizing the artificial neural network to generate apredicted-route-accuracy-metric, the predicted-route-accuracy metriccomprising at least one of: a predicted accuracy classifier, a predicteddistance, a predicted path, or a predicted GPS noise level.
 13. Thenon-transitory computer readable medium of claim 12, further comprisinginstructions that, when executed by the at least one processor, causethe computer system to: identify a ground-truth-route-accuracy metric,the ground-truth-route-accuracy metric comprising at least one of aground-truth accuracy classifier, a ground-truth distance, aground-truth path, or a ground-truth GPS noise level; and compare thepredicted-route-accuracy metrics to the ground-truth-route-accuracymetrics by applying a loss function to the predicted-route-accuracymetric and the ground-truth-route-accuracy metric.
 14. Thenon-transitory computer readable medium of claim 9, wherein the set oftraining GPS locations comprise simulated training GPS locations, andfurther comprising instructions that, when executed by the at least oneprocessor, cause the computer system to generate the simulated trainingGPS locations by: determining a road network comprising the trainingroute; creating simulated route locations for the training route withinthe road network; and generating the simulated training GPS locations bytransforming the simulated route locations based on a GPS-noise model byutilizing a diagonal matrix comprising a noise deviation and Weinerprocess.
 15. A method comprising: identifying a set of training GPSlocations and a set of training map-matched locations for a trainingroute; analyzing the set of training GPS locations and the set oftraining map-matched locations utilizing an artificial neural network togenerate predicted-route-accuracy metrics; training the artificialneural network to select between GPS locations and map-matched locationsby comparing the predicted-route-accuracy metrics toground-truth-route-accuracy metrics; and modifying a digital map byutilizing the trained artificial neural network to analyze GPS locationsfor a route travelled by a client device and map-matched locations ofthe digital map.
 16. The method of claim 15, further comprising:determining a set of training regions corresponding to the trainingroute; and generating a training route tile for a training region of theset of training regions based on a subset of training GPS locations anda subset of training map-matched locations corresponding to the trainingregion.
 17. The method of claim 16, wherein generating the trainingroute tile comprises: generating a first training image for the trainingregion based on the subset of training GPS locations; and generating asecond training image for the training region based on the subset oftraining map-matched locations.
 18. The method of claim 16, furthercomprising analyzing the training route tile utilizing the artificialneural network to generate a predicted-route-accuracy-metric, thepredicted-route-accuracy metric comprising at least one of: a predictedaccuracy classifier, a predicted distance, a predicted path, or apredicted GPS noise level.
 19. The method of claim 18, furthercomprising: identifying a ground-truth-route-accuracy metric, theground-truth-route-accuracy metric comprising at least one of aground-truth accuracy classifier, a ground-truth distance, aground-truth path, or a ground-truth GPS noise level; and comparing thepredicted-route-accuracy metrics to the ground-truth-route-accuracymetrics by applying a loss function to the predicted-route-accuracymetric and the ground-truth-route-accuracy metric.
 20. The method ofclaim 15, wherein the set of training GPS locations comprise simulatedtraining GPS locations, and further comprising generating the simulatedtraining GPS locations by: determining a road network comprising thetraining route; creating simulated route locations for the trainingroute within the road network; and generating the simulated training GPSlocations by transforming the simulated route locations based on aGPS-noise model by utilizing a diagonal matrix comprising a noisedeviation and Weiner process.